Method and system for cross channel in-car media consumption measurement and analysis using blockchain

ABSTRACT

A method and system for measuring and analyzing in vehicle media consumption and user interaction with a vehicle through an in vehicle entertainment system located in the vehicle. The in vehicle entertainment system receives media content. The method and system monitors both the media content and user interaction with the vehicle with content and interaction measurement software stored in the storage of the head unit of the vehicle as a module on the in vehicle entertainment system. The content measurement software directly records data relative to the media content being played or user interaction, in real time, as a data set and transmits the data set relative to the media content being played and user interaction to at least one immutable distributed ledger. The data set includes at least the local time of the start of the media content or the user interaction and is hashed.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of and claims priority fromearlier filed U.S. patent application Ser. No. 16/261,691, filed on Jan.30, 2019, which is a continuation-in-part of and claims priority fromearlier filed U.S. patent application Ser. No. 16/180,173, filed on Nov.5, 2018, which is a continuation-in-part of earlier filed U.S. patentapplication Ser. No. 14/872,497, filed Oct. 1, 2015, now U.S. Pat. No.10,165,070, which claims benefit to U.S. Provisional Patent ApplicationSer. No. 62/059,420, filed Oct. 3, 2014, the entire contents thereof areeach incorporated herein by reference.

BACKGROUND OF THE INVENTION

Research studies in the media industry typically, and consistently,indicate that more than 50% of radio listening is done in a vehicle,such as a car, and further that more than 50% of all audio listeningoccurs in such a vehicle. However, today there does not exist anyability to actually measure and effectively analyze what people arereally listening to, or watching, while in a vehicle—with accurate timeand/or location data tied to a distributed ledger.

At the outset, measurement and analysis of media consumption while in avehicle is important to multiple stakeholders, such as: 1) radio (andother content) advertising businesses to support the buying/selling andpricing of advertising (the US market for radio advertising alone beingvalued at $17 billion in 2013); 2) radio station owners and programmingmanagers to guide their selection of programming and on-air talent; 3)the music industry to gauge public reaction to artists and their work;4) the automotive industry to understand the behavior of their customerswhile in their vehicles; 5) and any other entity that may be interestedin the measurement and analysis of such media consumption.

There have been many attempts in the prior art to generate estimates ofthe use of in-vehicle audio content. For example, Nielsen Audio,previously Arbitron, provides a service to estimate the audience ofAM/FM radio stations, which is primarily based on periodic surveymethodologies using samples. These include use of log book/diaries,which are manually filled out by a limited sample of selectedparticipants, and the use of specialized devices such as Nielsen's“Portable People Meter” or PPM. A PPM is a small device worn or carriedby selected participants which identifies any AM/FM radio stations inearshot of the participant from identification signals embedded in eachindividual radio station broadcast. Other approaches have involved theuse of expensive specialized measurement equipment added to a samplenumber of vehicles.

However, these existing prior art methods have many disadvantages andproblems. As a result, these estimation methodologies are considered tobe outdated and inadequate to meet the current needs of stakeholdersbecause they, for example, suffer from: 1) small participant samplesize; 2) high cost of gathering data in this manner; 3) potential forthe statistical integrity of the approach to be compromised (whetherunintentionally or fraudulently) by the participants; 4) infrequentperiodic timing (only several times per year) with significant lag timebetween survey and report availability, thus not providing the potentialfor real-time monitoring and analysis desired by the industry; 5) lackof ability to comparatively measure “cross channel” audio consumption(e.g. AM/FM radio vs. SDARS vs. Internet Radio, etc.); 6) the lack ofability to measure all types of media consumption (e.g. audio, video,etc.); and at least 7) the lack of the ability to ensure the integrityof the data by immutability tying such data to a distributed ledgerschema.

Despite the foregoing limitations in the methodology used, Nielsen stillgenerated more than $450M from the sale of AM/FM radio measurement datafor the US market in 2013 as no viable alternative rating source data isavailable.

Nielsen utilizes panels of selected participants where they askquestions regarding audio usage and then extrapolates to the population.Nielsen also utilizes a PPM (portable people meter) which is a smallmetering device that is carried by a small group of people which listensto what audio is around them and can identify what stations are playingbased on code that is, embedded in a station's broadcast, to measure FMand AM radio. This too is a sample.

As another prior art example, Triton Digital measures Internet radiolistening utilizing server logs for each station/channel. Typically,each individual channel has access to this information as well fromtheir content delivery network.

In a further example, SiriusXM is not able to measure what channels itssubscribers are listening to as it is primarily a one-way broadcast viasatellites,

In view of the above, there is currently no comprehensive source of datafor the accurate measurement of the full spectrum of media content thatis actually consumed in an automobile. The currently available estimatesof in-vehicle audio listening are deficient in many ways, including: 1)Not real-time or near real-time (surveys conducted only several timesper year with considerable lag time before reports are available); 2) Donot cover all potential media sources (e.g. can estimate AM/FM radio butcannot estimate SDARS, internet radio, stored media, streaming media,etc.); 3) Unable to provide “cross-channel” comparison (e.g. between FM& SDARS); 4) Unable to measure content brought in to the vehicle via aconnected MP3 player, DVD/Blu-ray player, smartphone or other ConsumerElectronic (CE) device; 5) Survey-based methodology (rather than actualmeasurement); 6) Small survey participant sample size; 7) Significantvulnerability to bias and fraud; 8) High cost of data collection (boththe high cost of administering the survey participants and the high costof specialized monitoring equipment such as Nielsen's PPM device); 9)Provide minimal geographic location information; 10) Does not includeany accurate timing information for correlation with multiple sources;11) are unable to provide detailed information on which advertisingcommercials a user heard, how and where they were heard, and whether theuser took action as a result of hearing the ad, etc.

The clear industry requirement, not met by any existing system, is for acomprehensive capability that measures all forms of media consumed inthe vehicle including, but not limited to, terrestrial AM/FM, HD Radio,SDARS (SIRIUS XM), Internet radio and audio/video streaming services(e.g. PANDORA, TUNEIN, SPOTIFY, RDIO, SONGZA, YOUTUBE, etc.), personalmedia collection (CD, MP3, podcast, DVD, Blu-ray, etc.), audio books,podcasts, text-to-speech, use of hands-free calling and other audio,including content routed to the In Vehicle Entertainment (IVE) systemthrough integration with a smartphone, MP3 player or similar external CEdevice (via wired or wireless connectivity, including but not limited toUSB, BLUETOOTH, Wi-Fi, etc. and including various platforms forin-vehicle smartphone integration such as APPLE CARPLAY, GOOGLE ANDROIDAUTO, HARMAN AHA RADIO, PANASONIC AUPEO, PIONEER ZYPR, FORD SYNC,MIRRORLINK, AIRBIQUITY CHOREO, etc.).

Another clear requirement, which is not met by any existing system isthe need to facilitate low-cost, large-scale deployment to supportmeasurement from a large user sample to ensure a high level ofstatistical integrity and accuracy. Existing approaches using a)survey-based methodologies or b) methodologies requiring specializedequipment that needs to be installed in a vehicle do not provide thepotential to meet this objective in a viable and cost-effective manner.

To meet industry expectation, there is a need for a system to be able tocontinuously provide measurement data in real-time and with a highdegree of geographic location accuracy. A large sample size, asidentified above, is also a pre-requisite of achieving this requirement.

Still further, having developed a system and methodology to actuallymeasure the media content, including audio and video, consumed in avehicle, there is also a demand for a differentiation between multipleusers of the vehicle (e.g. members of the same family). This includescontextual analysis of how media consumption may differ with situation(e.g. a mother or father may primarily listen to adult news and musiccontent during their commute while alone in the car but might listen tokids channels whenever their children are in the car).

Further still, the instant system and methodology allows for themeasurement of data, audio, and video as well as other content deliveredto a vehicle. For example, a vehicle can receive a display ad, coupon,cryptographic token, audio, video or other content relating to a fastfood restaurant. Using data associated with what is beingbroadcast/transmitted to the vehicle as well as location data from thesystem, the instant method and system can determine whether the vehicletook an action, e.g. drove to the store or accessed a website, saved theinformation for later, etc. This analysis is also known as adattribution. Comparing the activity of vehicles that viewed/heard an adto the vehicles that did not view/hear the ad results in the ability tomeasure video store ad conversion rates, number of store visits,advertising lift and ad cost per store visit. Combining this data withconsumer store spending data leads to a value per vehicle visit. UsingGPS location data derived from the vehicle the system can developdriving patterns, store visit locations, and visited store types whichcomprise valuable intelligence for retailers. Such intelligence mayinclude, for example, metrics describing the frequency, timing, numberand type of visits/occurrences, repeating patterns, financial and othervalue exchange, redemptions, purchase analytics and the like. The impactmay be measured using a variety of methods such as including bothquantative and qualitative, as are typically employed in analysis ofadvertising, promotion and marketing campaigns, such as conversionfactors, upsell analytics, engagement metrics (such as time in store)and the like which are well known in the art. Over time, visitationtrends and macro and micro level events affecting real world behaviorcan be determined. Combining vehicle location data with consumerdemographics, mobile devices and app usage delivers the most accurate adtargeting capability.

In addition to the aforementioned benefits, the instant system andmethodology allows for the acknowledgement that information receivedfrom a vehicle entertainment system is partial in nature, in that suchinformation does not convey the context of the experience of a vehicle'soccupants for a media event. The combination of verifiable proof ofperformance, through accurate time and location alignment combined withmultiple media sources and contextual information provides a rich,accurate and immutable record of a vehicles occupants experiences. Theinformation sets for traditional methods, center on a single source, avehicle's inbuilt radio, whereas modern vehicles today includeentertainment systems that support, radio, other hard media such as CD,USB and the like and connected sources such as embedded wireless modemsand smart phones. Even the purveyors of systems that track podcasts, oneof the most rapidly growing media sources of today, state that listeningbehavior cannot be monitored.

The use of an immutable repository, such as a distributed ledger, torecord the timing and location information is complemented by the use ofcryptographically bound containers, which span a period of time, with agranularity that can differ from the underlying blocks of thedistributed ledger to provide further benefits over the prior artsystems.

The foregoing attempts in the prior art fail to meet the needs of theindustry, and the various stakeholders thereof. There exists significantindustry demand, from the stakeholders identified above, for a morecomprehensive in-vehicle media consumption measurement system that canprovide greater accuracy, finer granularity and real-timemeasurement/analysis of media content consumption across all applicablesources—such a system does not exist today.

SUMMARY OF THE INVENTION

The present invention preserves and improves the advantages of prior artmonitoring, listening, and reporting systems and methods for crosschannel in car media, including audio, video, local, and web-basedapplications and games, and other media types. In addition, it providesnew advantages not found in currently available systems and methods andovercomes many disadvantages of such currently available systems andmethods.

The invention is generally directed to the novel and unique system andmethod for cross channel in car media consumption measurement andanalysis.

The invention meets the above-identified needs by providing a system,apparatus, method and computer software for obtaining, measuring andanalyzing in real-time (or on such other basis that can be configured)all forms of media content that a driver or passenger may consume insideof an automobile in combination with a reference time base and areference location base, both of which are immutably recorded in a formthat can be independently verified. This includes, but is not limitedto, AM/FM radio, Satellite Digital Audio Radio Service (SDARS), storedmedia such as CDs, MP3s, DVDs and MP4s, content streaming, internetradio, audio books, podcasts, text-to-speech content and other forms ofcontent, including content routed to the In Vehicle Entertainment (IVE)system through integration with a smartphone, MP3 player, DVD/Blu-rayplayer, game console or other similar external Consumer Electronic (CE)device (via wired or wireless connectivity, including but not limited toUSB, BLUETOOTH, Wi-Fi, etc.). The combination of an accurate timereference, and accurate location reference and multiple sources ofinformation, which is represented by an event generated initially inreal time, provides a unique insight into the multifaceted behaviors ofvehicle occupants as they undertake journeys and are exposed to multiplesources of content.

Of particular significance is that the invention allows for theacknowledgement that information received from a vehicle entertainmentsystem is partial in nature, in that such information does not conveythe context of the experience of a vehicle's occupants for a mediaevent. The combination of verifiable proof of performance, throughaccurate time and location alignment combined with multiple mediasources and contextual information provides a rich, accurate andimmutable record of a vehicles occupants experiences. The use of animmutable repository, such as a distributed ledger, to record the timingand location information is complemented by the use of cryptographicallybound containers, which span a period of time, with a granularity thatcan differ from the underlying blocks of the distributed ledger toprovide further benefits over the prior art systems.

The instant approach incorporates the use of a reference time base and areference location base, both of which are immutably recorded in a formthat can be independently verified. This time base and location base arethen used to record events which are captured through the vehicle headunit (VHU). The events that are captured include those associated withthe media and/or vehicle operations of the VHU. One embodiment of theoverall system configuration provides for event sets which can begenerated by at least one event source, such as a vehicle VHU or proxythereof. The time and location information included in that event, canthen be validated for integrity, including accuracy. The timingintegrity processing, in part, can use a trusted time reference source,such as those provided by NIST, trusted network time or other referencesources. The location integrity processing, in part, can use a trustedlocation reference source, such as those provided by the GlobalPositioning System (GPS), or other reference sources. These events,complete with timing and location information, can be recorded in animmutable trusted set of distributed ledgers. Further event informationsets, which have had their timing and location information integrityprocessed, and then correlated with the original event information setsand can be recorded in the appropriate trusted distributed ledgers.Analysis processing can be undertaken by a data analytics pipeline,comprising a set of analytics tools in any arrangement, which may thenoutput information sets to both distributed ledgers and/or otherrepositories that are bound to those ledgers, and repositories formeasurements, insights and reports.

In addition to the time and location related events generated by a VHU,further information sets may be integrated from additional sourcespertaining to those events. These information sets may then becorrelated with VHU event information sets to form further informationsets. In some embodiments, such information sets may be bound to one ormore reference timelines and/or one or more reference location bases tocreate an integrated multi source record of such events.

Additionally, the invention is able to measure “cross channel”in-vehicle media consumption consistently and comparatively acrossmultiple content types and sources (e.g. AM/FM radio, SDARS, internetradio, stored media, satellite video, terrestrial video, IP streamingvideo, ATSC 3.0 broadcasts, etc.).

Also, of note is the invention's ability to provide not only betterinformation on what content is being consumed, but incrementalcontextual information on how listeners respond to this content (such aschanging station or skipping forward when they don't like what isplaying, turning up the volume on favorite tracks, thumbs up, etc.).This incremental contextual information on how listeners respond tocontent for the first time provides the potential for a “feedback loop”to the creators/programmers of the applicable content (for example,allowing AM/FM radio stations to better understand how listeners respondto their broadcast, thus allowing them to enhance their programming tobetter meet their listener's preferences).

Another key factor is the invention's ability to measure in-vehiclemedia consumption using a much larger sample size than ever beforecontemplated due to the architectural approach that fully supportslow-cost, large-scale deployment in millions of vehicles.

Also important is the invention's ability to provide real-time dynamicmeasurement of in-vehicle media consumption (compared to the extensivelag time between survey and report of the existing methodologies).Alternatively, the system can permit for real-time or periodicmonitoring of the use of audio, video, display content and related datain a vehicle, via software installed in the head unit of a vehicle alongwith hardware to receive the data, audio and video signals/channels.

The measurement data and analysis from the invention may be provided toauto manufacturing companies, providers of media content (includingthose available currently and others that may be available in thefuture), advertising companies, platforms and agencies, the musicindustry and other interested parties.

The objective of the invention is to measure all applicable forms ofmedia consumption in an automobile and to provide an immutable record ofthe time and location data associated with some or all of themeasurements. This consumption will represent actual measured datarather than mere survey data (which is the only data available today).

Actual measurement and analysis of what media people consume while in avehicle is important to multiple stakeholders, who are currentlyunder-served by existing measurement services based on surveymethodologies, including (but not limited to: 1) radio, television (andother content) advertising businesses to support the buying/selling andpricing of content advertising (the US market for radio advertising isvalued at $17 billion in 2017 and television advertising revenue of $70billion); 2) radio and television station owners and programmingmanagers to guide their selection of programming and on-air talent; 3)the music industry and video production companies to gauge publicreaction to artists and their work; 4) the automotive industry tounderstand the behavior of their customers; and 5) and any other entitythat may be interested in the measurement and analysis of such mediaconsumption.

The invention has been developed to provide a new level of in-vehiclemedia consumption measurement capability achieving the followingobjectives:

-   -   1. A substantially higher level of accuracy and granularity by        using actual data measurement from a large sample population        (rather than survey measurement from a small sample).    -   2. Provide an approach that makes large sample measurement        viable by significantly reducing the per-vehicle installation        and operational cost, and thus allowing widespread deployment in        millions of vehicles    -   3. Provide the ability for real-time measurement and analysis,        to meet the industry requirement for dynamic data.    -   4. Provide the ability to measure content from multiple sources        in a consistent and comparable way (to include broadcast or        internet services such as video sources, AM/FM radio and SDARS,        personalized services such as PANDORA, IHEART RADIO, HULU,        NETFLIX, etc., stored media content such as CD, MP3 and        DVD/Blu-ray players and content sourced from a connected CE        device (including various platforms for in-vehicle smartphone        integration such as APPLE CARPLAY, GOOGLE ANDROID AUTO, HARMAN        AHA RADIO, PANASONIC AUPEO, PIONEER ZYPR, FORD SYNC, MIRRORLINK,        AIRBIQUITY CHOREO, HULU, VIMEO, YOUTUBE, COX, COMCAST, VERIZON,        NETFLIX, HBO, AMAZON PRIME, etc.).    -   5. Provide more detailed metadata relating to what is actually        consumed (such as song title, artist name, etc.). Such metadata        may be achieved both through direct collection in the IVE and        also through timestamp matching of the media source (e.g. a        particular satellite radio channel) with a play list of the same        content source captured separately.    -   6. Provide contextual data relating to the user's consumption        behavior (such as turning up the volume during a favorite song,        changing channel when the DJ is annoying, etc.)    -   7. Support analysis of different consumption habits in vehicles        when used by different people (e.g. members of the same family)        and in different situations (e.g. commuting alone vs. a weekend        family road trip).    -   8. Allow determination of vehicle user demographics by merging        and cross-referencing available, known data (such as vehicle VIN        and vehicle owner information) with other sources of third-party        data (such as cell phone UDID and user data) to provide more        comprehensive analysis of vehicle usage and operator        demographics.    -   9. Provide geographically referenced data to allow a more        complete pattern of user behavior to be determined.    -   10. Provide listening and viewing habits in all makes and models        of vehicles and in varying regions of the U.S.    -   11. Provide an industry-scale platform with capabilities for        future expansion of additional capabilities, including but not        limited to, such services as verification of audio and video ads        (including the specific ID of such audio/video ad) actually        played in vehicles, delivery of personalized audio/video ads        into the vehicle, etc.    -   12. Provide an immutable record of all such collected data to        ensure the accuracy and veracity of the data sets which in turn        provides added value to the measurements and resulting        determinations and reports.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features which are characteristic of the present invention areset forth in the appended claims. However, the invention's preferredembodiments, together with further objects and attendant advantages,will be best understood by reference to the following detaileddescription taken in connection with the accompanying drawings in which:

FIG. 1 is a diagram of one embodiment of the data analytics processingsystem;

FIG. 2 shows one embodiment of multiple reference ledges derived from asingle trusted time reference;

FIG. 3 shows two event frameworks created from a single head unit;

FIG. 4 shows an example of media information being added to an eventframework;

FIG. 5 shows example event sets with a media source;

FIG. 6 shows an example event used for ad attribution;

FIG. 7 shows one embodiment of the system architecture;

FIG. 8 shows one embodiment of an event container;

FIG. 9 shows one example of vehicle generated events with a trustedtimeline;

FIG. 10 shows one example of an event framework;

FIG. 11 shows one example of an event framework being bound to an eventcontainer, to an event ledger, and to an event repository;

FIG. 12 shows an example of an event container and event frameworksalignment to a trusted timeline;

FIG. 13 shows an example of event containers bound to blocks in a timereference ledger;

FIG. 14 shows an example of contextual and other supporting eventinformation added to an event framework; and

FIG. 15 is a diagram of an embodiment of the data analytics processingsystem.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring to FIGS. 1-15 of the present invention, it is common today forcontent from many different sources to be consumed in a vehicle, and forthese sources to utilize a multitude of technologies to deliver thecontent into the vehicle (e.g. AM/FM radio, SDARS, stored media players,cellular wireless, IP technologies, terrestrial and satellite videosignals, ATSC 3.0, BLUETOOTH integration of CE devices, etc.). In orderto effectively and comparatively measure actual media consumption fromany and all of these sources, it is necessary to conduct measurement ata point in the system that has visibility of content played from any andall of these sources. The In Vehicle Entertainment (IVE) system, alsoknown as the vehicle “head unit” is the only point at which content fromeach and every source can be measured—this is because the IVE controlsall media playback in the vehicle through the built-in amplification,speaker systems and display screens. The information sets fortraditional methods, center on a single source, a vehicle's inbuiltradio, whereas modern vehicles today include entertainment systems thatsupport, radio, other hard media such as CD, USB and the like andconnected sources such as embedded wireless modems and smart phones.Even the purveyors of systems that track podcasts, one of the mostrapidly growing media sources of today, state that listening behaviorcannot be monitored.

A key tenet of the system described herein is acknowledging thatinformation received from a vehicle entertainment system is partial innature, in that such information does not convey the context of theexperience of a vehicle's occupants for a media event. The combinationof verifiable proof of performance, through accurate time and locationalignment combined with multiple media sources and contextualinformation provides a rich, accurate and immutable record of a vehiclesoccupants experiences.

The use of an immutable repository, such as a distributed ledger, torecord the timing and location information is complemented by the use ofcryptographically bound containers, which span a period of time, with agranularity that can differ from the underlying blocks of thedistributed ledger.

The invention is generally directed to the novel and unique system andmethod for cross channel in-car media consumption measurement andanalysis. As generally shown in FIG. 1, this approach incorporates theuse of a reference time base and a reference location base, both ofwhich are immutably recorded in a form that can be independentlyverified. This time base and location base are then used to recordevents which are captured through the vehicle head unit (VHU). Theevents that are captured include those associated with the media and/orvehicle operations of the VHU, e.g. operation of the entertainmentsystem, vehicle control systems (e.g. windshield wipers, lights, turnsignals, etc.), the vehicle ignition, etc. FIG. 1 illustrates anembodiment of the overall system configuration, including event sets 101which are generated by at least one event source, such as a vehicle VHUor proxy thereof. The time and location information included in thatevent, is then validated for integrity, including accuracy 102, this canbe performed locally or remotely on a separate server. The timingintegrity processing, in part, uses a trusted time reference source,such as those provided by NIST, trusted network time or other referencesources 103. The location integrity processing, in part, uses a trustedlocation reference source, such as those provided by the GlobalPositioning System (GPS), or other reference sources 109. These events,complete with timing and location information, are recorded in animmutable trusted set of distributed ledgers 107. Further eventinformation sets 104, which have had their timing and locationinformation integrity processed, and then correlated with the originalevent information sets and are recorded in the appropriate trusteddistributed ledgers. Analysis processing can be undertaken by a dataanalytics pipeline 105, which can include a set of analytics tools inany arrangement, which may then output information sets to bothdistributed ledgers 107 and/or other repositories 106 that are bound tothose ledgers, and repositories for measurements, insights and reports.

In use, each event will occur at a specific time and location, where thetime attributes for that event can be configured as a start, duration,and end times. All three times may be coincident or may vary dependingon the type of event being monitored. The recording of such time andlocation, of the event, is dependent on the local measure of time andlocation being utilized. For example, a Vehicle Head Unit (VHU) in thevehicle may have an internal time reference with which events created ormonitored by such VHU are recorded with, for example, at least one timestamp and may include a Global Navigation Satellite System (GNSS) whichprovides at least one set of location co-ordinates.

These time stamps may be locally accurate, however they may differ to agreater or lesser degree to an accurate reference time, such as thatprovided by the National Institute of Standards and Technology (NIST),network time and the like. A system may select a reference time which isstored in an immutable format, for example as a blockchain, and any VHUgenerated time stamp can then be correlated to such a reference time tocreate an offset for that VHU such that the events of that VHU areaccurately correlated to the reference time.

This correlation may be configured through use of multiple sources forthe same event, aggregation of multiple VHU time information sets,integration of co-located devices with a reference time, for example asmart phone with network time and other well-known techniques.

The system can establish a reference and accurate time line based on atleast one network/reference time. This time reference can be recorded insuch a manner that the time the recorded time is immutable, for exampleusing distributed ledgers employing at least one blockchain.

A timeline, in some embodiments may comprise sequential time increments,measured in units of time, for example seconds, minutes or portionsthereof. These time units may form segments with differinggranularities, where each segment is bound to the reference time in amanner that is immutable. For example, a reference time line may havebound to it multiple segmented time spans, where such segments arealigned with the types of content to be monitored and/or may form partof an event container or other event information managementconfigurations.

In FIG. 2, a reference time line 201 can provide a single trusted pointof reference for all reference time ledgers 202, 203, 204 bound to thiscommon trusted time reference 101. Each of the time ledgers 202-204 maycomprise segments of differing granularity to which events may be bound,through for example event containers 210, 212, 214. In the illustratedexample, the reference time ledgers 202-204 can be aligned to eventscaptured that are content specific, such as advertising parametersincluding advertising segments and radio programs, podcasts or otherlinear programming.

The time information that comprises each of the time ledgers 202-204,may in some embodiments, be expressed as a differential from a startingtime T0, see FIG. 9 for example, where such a time is cryptographicallyexpressed such that T0 is not able to be calculated from the informationin the time ledger. For example, information may be represented from astart time T0, such as indicated by a vehicle event, and the subsequentevents may have their times represented relative to that time. Thisinformation may be useful when presenting information that refers to thejourney conditions, such as time travelled at specific speed bands,numbers of halts (at stop signs or traffic lights) or other journeyinformation, such that the media events are represented within thiscontext. For example, changing from an entertainment stream to a localradio station for traffic information or seeking a particular style ofmusic and the like. This information may also include reports to mediacustomers where the time is based on the start of an event, such as aparticular song or program where the length of time or the volumecontrol or other audio control use is of interest.

An event, from the event set 101, can be an information set that isgenerated as an outcome of at least one action. Each event can includeat least one time stamp and at least one set of information thatdescribes an action. A location can be associated with this event, asnoted above. A unique identifier (UID) can be associated with such anevent, where this identifier is sufficiently unique to supportevaluation, management and/or processing of that event. Such a uniqueidentifier can be used to anonymize a user such that no identifiableinformation about the user is transmitted to the data analytic pipeline.

Events may be created or generated from multiple sources including VHUand/or proxies thereof, broadcast and other content sources, one or moredevices connected to a VHU, aggregation of VHU information sets,including those that have undergone one or more processing and/orconfiguration steps, and/or other sources.

Each event has a time stamp or may occur within a specific time period.In all cases, the system will normalize the time information to conformto an underlying reference time ledger 202-204, such that the event isimmutably bound to the underlying time reference 205.

This normalization process may be configured to incorporate theinformation provided in the event information set, information derived,extracted or processed from other information sources that have beendetermined to be related to that event and/or other information sourcesthat increase the accuracy, consistency and other metrics of theresulting time information.

In some embodiment's events may be of differing types, as illustrated inFIG. 3, where each event is identified as, for example, a media event302 or a vehicle event 301 and in some cases other types that may beclassified as event types. These event types 301, 302 may then beincorporated into an event framework 311, 312. These events may becombined into a single event framework for some operations, though eachevent type is an attribute of such event. The assignment of the UID foreach type may take place in the VHU, on receipt by an event framework orby other processes subsequent to the event instantiation.

Time correlation 320 and event correlation 330 illustrate an embodimentwhere these respective processes are undertaken prior to such eventframeworks being bound to at least one event container including arepository of event containers 350 that have been instantiated and arebound to a reference time ledger via an alignment model 340.

Each of the aforementioned events can include at least one of time andevent information sets 311, 312. Time information can be expressed as atleast start and end and may include duration. The time information mayinclude one or more timestamps of any granularity or accuracy. Forexample, an event may occur over a time period, for example a minute orsimilar. In some embodiments, time stamps may be evaluated, including toestablish their accuracy and may be configured, though normalizationprocessing, to conform to an immutable time line. The time informationmay include correlating an event with other records of such an event toestablish an accurate time, where for example the granularity of thattime is insufficiently accurate. For example, if the time information isprovided at a granularity of minutes, this may be evaluated andvalidated against other more accurate timing information for the sameevent so as to ascertain a more accurate timing.

FIG. 4 illustrates an example of a media event generated by a VHU 10 orproxy thereof, instantiated as an event framework 312, with a time stamp314, which when evaluated for accuracy by a time correlation process 320in light of a further media source 12 which has been determinedrepresent the same event, through an event correlation process 330 andhas been determined to be more accurate when compared with the referencetime line ledger e.g. 202-204, corrects the initial time stamp 314 as anormalized time, configured in some embodiments as a normalized timelinefor that originating VHU 325, and writes this correlation as an offset316 to the original time stamp 314, aligning the event framework thatincludes the at least one event to the reference time ledger. The othermedia sources 12 may be evaluated and processed by a normalizationprocess module 334, which extracts the event information sets 332 andthe time information sets 336 from such other media sources and passesthese to the event correlation 330 and time correlation 320 processesrespectively.

As noted above, in addition to, or in place of timing measurements, oneaspect of the system is the relationship between the experience of avehicles occupants and the locations at which that experience and theevents generated thereby occurred. As a vehicle moves from one locationto another at various times, the content they are exposed to may vary,for example as vehicle undertakes a journey from one market area toanother or moves form one local radio station area to another. This mayinclude long distance travel, such as when occupants of a vehicleundertake a holiday or similar.

The granularity of this location information may, in part, be determinedby VHU information derived from vehicle GPS or other navigation systems.In some embodiments this may be augmented and/or updated by other deviceinformation, such as from a smart phone using a mapping and/ordirections system. The availability of this information may depend, inpart, on occupant providing access to this information independent ofthe VHU, for example through an application with an opt in functionand/or through integration with a VHU whereby the location informationis passed to the VHU.

Location information may have limited accuracy and granularity,depending on the source of this information and the commercial andregulatory limitations that are in operation at the time. Locations maybe mapped for a vehicle or set thereof, such that a set of waypoints andassociated time stamps are identified for repeated journeys, such that apattern that represents such a journey may be retained in at least onerepository and media and/or vehicle events tracked against that pattern.These waypoints may be correlated to one or more digital maprepresentations and as such waypoints can be expressed in terms of theroads that the vehicle is traversing during the journey. In this mannerany variations in the received location data are normalized to the roadsthat are available for the vehicle to operate upon. In this manner, whenan event is inconsistent with a pattern an exception may be raised whichcan then be used for further processing. For example, the exception mayinitiate a further location tracking granularity, in relation tolocations associated with the content being broadcast or experienced atthat time. For example, if an offer is made by an advertiser and avehicle with an established journey pattern undertakes a deviation fromthat pattern to the location of the provider of the offer, certain realtime effects may be configured and instigated. For example, if theuser's devices have a connection to the VHU which includes a uniqueidentifier, such as a MAC address or BLUETOOTH ID, then this informationmay be provided to the destination location and a user on entering suchlocation may be presented with an offer or set thereof.

In some embodiments, each event may include a reference to at least onelocation, for example a VHU may provide location information from thevehicle global navigation satellite system (GNSS) or similar and/or anentertainment device used by at least one occupant may communicatelocation information. These attributes may be the vehicle specificand/or may be aggregated into sets of vehicles. These locations may bereferenced to a mapping systems, which includes the locations of PointsOf Interest (POI), such as buildings, infrastructure, roadways and thelike. This may include POI that are associated with advertisers whosecontent is available to a vehicle's occupants.

In some embodiments where a vehicle includes a wi-fi hotspot theintegration of a user's devices may be such that each of the occupant'sindividual experiences may be monitored, through determination of theunique identity of the vehicle and any devices operating therein, forexample, a MAC address or other device identification, and whereappropriate the broadcast, multicast, stream and/or communicationsmethods with which those devices are associated.

FIG. 5 illustrates an example event sets 501 a, 501 b, 501 c forvehicles and media event sets 530 which may form, in whole or in part asuch a pattern 550. An example vehicle event set 501 c may comprise anumber of information elements, such as those illustrated in FIG. 5,including vehicle identity (which may be anonymized), time stampinformation, location information, vehicle characteristics, event type,media source and session characteristics. For each of these elementsthere may be at least one data element, such as a key value pair,attribute value pair, name value pair or other information organizationformat. In some cases, there may be multiple information sets organizedin any arrangement, however each of which having an associated timestamp.

An example media event 530 may span a session, representing a set ofmedia events over a time period and/or a journey. In this example eachelement may also comprise at least one data element, such as a key valuepair, attribute value pair or other information organization format. Inthis example time stamps are classified by their occurrence so as tocreate a timeline for the session.

FIG. 5 additionally illustrates the use of distributed ledgers, for bothidentity and time information 205, 202-204, providing an immutablerecord of the occurrence of these events related to a trusted referencetime source. Example patterns are also illustrated in FIG. 5, where sucha pattern may include a set of elements, including those directlyderived from the vehicle and media events and further information setsthat have been correlated to the events of both the vehicle and mediaevent sets. For example, the type of journey may be classified, such asthe AM/PM commute illustrated in FIG. 5. The weather for the journey mayalso be included, as may traffic information, including probabilitybased metrics, which may have influenced the selection of media sources,and consequently media event s for the journey. Other elements mayinclude market segmentations or other market associated classificationswhich may then be used in the requisite reports provided by the overallsystem.

In addition to the timing and location information, combinations ofinformation representing a state of a vehicle or a vehicle'sinfotainment head unit at the moment an event occurs can be representedas an information set, including event attributes such as thosedescribed herein. The following are examples of event attributes withinan example framework of events:

“eventReport”: {  “header”: {  “reportId”:“9F7648C765964A23AC45708726903709”,  “anonymousCarId”: “XY0677052M92”, “agent”: “OEM1-HU02-AGENT3”,  “carAgentVer”: “2.2”,  “protocolVer”:“1.1.0”,  “timestamp”: “2018-08-31T09:07:34Z”,  “locationAccuracy”: 3 },“eventSet”: [{  “eventId”: “D548C5D59C8F4F71A2D4B20341524D6B”,  “event”:“CAR_STATUS_CHANGE”,  “timestamp”: “2018-08-31T07:05:25Z”,  “location”:{“type”:“Point”, “coordinates”:[−118.138,34.154]  },  “attributes”:{“ignition”:“On”  }  },{  “eventId”: “D77005DCAAC44772BCA977749B4DD502”, “event”: “VHU_MEDIA_SOURCE_CHANGE”,  “timestamp”:“2018-08-31T07:05:25Z”,  “location”:{ “type”:“Point”,“coordinates”:[−118.138,34.154]  },  “attributes”:{ “source”:“CD”,“volume”:24 }  },{ “eventId”: “ 59CCB9298378424398D174C320FD0605”,“event”: “VHU_MEDIA_SOURCE_CHANGE”, “timestamp”: “2018-08-31T07:06:30Z”,“location”:{  “type”:“Point”,  “coordinates”:[−117.989,34.092] },“attributes”:{  “source”:“Tuner”,  “band”:“FM”,  “frequency”:“89.3” “volume”:24 }  },{ “eventId”: “0BD9A1DB40144FF28218F679ECB589DE”,“event”: “VHU_VOLUME_CHANGE”, “timestamp”: “2018-08-31T07:06:40Z”,“location”:{  “type”:“Point”,  “coordinates”:[−117.989,34.092] },“attributes”:{  “volume”:30 }  },{ “eventId”:“145B530D962F47A998EDB8CEAACE9033”, “event”: “VHU_STATION_CHANGE ”,“timestamp”: “2018-08-31T08:36:33Z”,  “location”:{ “type”:“Point”,“coordinates”:[−118.138,34.154]  },  “attributes”:{ “source”:“Tuner”,“band”:“FM”, “frequency”:“91.9” “volume”:30  }  },{  “eventId”:“15388931C35646C28D7F80E9B345BA54”,  “event”: “CAR_STATUS_CHANGE”, “timestamp”: “2018-08-31T09:07:30Z”,  “location”:{ “type”:“Point”,“coordinates”:[−117.805,34.111]  },  “attributes”:{ “ignition”:“Off”  } } ]

An information set is expressed as the content of the event, such as forexample a change in the controls of a VHU, including for example changein volume, content source, radio station, muting, incoming or outgoingphone call and the like. The expression of such event information mayvary according to the information sets received from the VHU and maythen be configured to a consistent format for further evaluation and/orcorrelation with other information sets representing events.

In some embodiments, the level of detail provided by a VHU (and/oraggregations thereof) may not match a level of detail sufficient toaccurately match other representations of that event. This situation maybe mitigated by applying one or more probability techniques to ascertaina level of certainty that two events may match each other. This isconsidered further below. Information comprising an event may beevaluated for, accuracy, specific one or more features, one or morecontent attributes and/or other characteristics. The following shows apossible result of correlating the event attributes from the previousexample with market geographic boundaries and radio station information:

“eventReport”: {  “header”: {  “reportId”:“9F7648C765964A23AC45708726903709”,  “anonymousCarId”: “XY0677052M92”, “agent”: “OEM1-HU02-AGENT3”,  “agentVer”: “2.2”,  “protocolVer”:“1.1.0”,  “timestamp”: “2018-08-31T09:07:34Z”,  “locationAccuracy”: 3, “process”: [“MARKET-ANNOTATION-1.01”, “RADIO-STATION-ANNOTATION-1.8”] },  “eventSet”: [{ “eventId”: “D548C5D59C8F4F71A2D4B20341524D6B”,“event”: “CAR_STATUS_CHANGE”, “timestamp”: “2018-08-31T07:05:25Z”,“location”:{  “type”:“Point”,  “coordinates”:[−118.138,34.154] },“attributes”:{  “ignition”:“On” }, “MSA”: “Los Angeles”  },{ “eventId”:“D77005DCAAC44772BCA977749B4DD502”, “event”: “VHU_MEDIA_SOURCE_CHANGE”,“timestamp”: “2018-08-31T07:05:25Z”, “location”:{  “type”:“Point”, “coordinates”:[−118.138,34.154] }, “attributes”:{  “source”:“CD”, “volume”:24 }, “MSA”: “Los Angeles”  },{ “eventId”:“59CCB9298378424398D174C320FD0605”, “event”: “VHU_MEDIA_SOURCE_CHANGE”,“timestamp”: “2018-08-31T07:06:30Z”, “location”:{  “type”:“Point”, “coordinates”:[−117.989,34.092] }, “attributes”:{  “source”:“Tuner”, “band”:“FM”,  “frequency”:“889.3”  “volume”:24,  “station”:“KPCC-FM” },“MSA”: “Los Angeles”  },{ “eventId”: “0BD9A1DB40144FF28218F679ECB589DE”,“event”: “VHU_VOLUME_CHANGE”, “timestamp”: “2018-08-31T07:06:40Z”,“location”:{  “type”:“Point”,  “coordinates”:[−117.989,34.092] },“attributes”:{  “volume”:30 }, “MSA”: “Los Angeles”  },{ “eventId”:“145B530D962F47A998EDB8CEAACE9033”, “event”: “VHU_STATION_CHANGE ”,“timestamp”: “2018-08-31T08:36:33Z”, “location”:{  “type”:“Point”, “coordinates”:[−118.138,34.154] }, “attributes”:{  “source”:“Tuner”, “band”:“FM”,  “frequency”:“91.9”  “volume”:30,  “station”:“KVCR-FM” },“MSA”: “Los Angeles”  },{  “eventId”:“15388931C35646C28D7F80E9B345BA54”,  “event”: “CAR_STATUS_CHANGE”, “timestamp”: “2018-08-31T09:07:30Z”,  “location”:{ “type”:“Point”,“coordinates”:[−117.805,34.111]  },  “attributes”:{ “ignition”:“Off”  }, “MSA”: “Los Angeles”  } ]Example of Ad Attribution

In the example illustrated in FIG. 6, personally identifiableinformation (PII) of the occupants, in this example the driver, 602and/or their vehicle 601 is stored separately in encrypted data storage.References to this information as well as business-relevant attributesof vehicle and/or occupants are recorded on an immutable identificationledger 603 as the identity ledger.

The location-time combinations of the events in the event set for thevehicle are also stored separately in a location-time data repository604 (which may be encrypted) and references to them are recorded in thetime reference immutable ledger, illustrated as time reference ledger605.

A set of listening experience sessions can be obtained by combining theevent set 730 with reference data, as illustrated in FIG. 7. Everyparticular listening experience will then contain business-relevantattributes, such as source and station, along with references toassociated location-time references. When necessary, the location-timereference or the anonymized vehicle identifier can be used toverify/certify the experience session, by reviewing the referencesstored the identification ledger and in the time reference ledger. Thelocation-time references stored in the ledger can be verified in thelocation-time repository, by authorized agents with appropriate accessrights.

The listening experience attributes, as shown in FIG. 6, can be comparedwith ad play logs 607, identifying, in whole or in part, overlapsbetween ad play times with listening session times on the same mediasources (e.g. radio stations), in order to generate ad exposure sessions608, which can be combined with individual attributes, such asdemographic classification, and aggregated from multiple vehicles toproduce measurements of total exposure of an ad campaign, such as numberof unique exposures, exposures per station, per demographic group, permarket area, per time of day, per day of week, exposure rate, etc.

Additionally, in some cases location-time combinations from the eventsets and from other sources can be compared with advertised businesslocations in order to identify visits made by reporting vehicles to suchbusiness locations and co-relate such visits with exposures to relatedads in order to identify conversions, which represent visits to anadvertised business that are likely caused by one or more exposure toads. These visits and ad conversions can also be combined withindividual attributes, such as demographic classification, andaggregated from multiple vehicles to produce measurements such asexposure-visit rate, visits or conversions by day, by time of day, bydemographic group, by market area, conversion rate, etc.

Ad exposure sessions, visits, ad conversions and the resultingaggregates can be verified, by reviewing the references stored in theidentity and time reference ledger and by confirming the informationreferenced from therein and stored in separate PII and location-timerepositories.

Event Monitoring

In addition to the ad-attribution scenario described above, events canbe monitored by multiple system elements, including those incorporatedinto a VHU, in communication with VHU and/or other event generationsources. In some embodiments, an initial action is generated by a VHU,with the system responding to an action by initiating at least one eventmonitoring system operation, where such event monitoring is configuredto capture such generated action. This initial event may be recorded ina manner that is immutable, for example it may be written to adistributed ledger, such as a blockchain and/or be hashed so as tocreate a unique record of the event.

In some embodiments, each event has a set of attributes including:

1) User Identification (UID), which may contextually or system wideunique

2) At least one timestamp

3) At least one location

4) At least one information set pertaining to event

5) An event classification/type

6) At least one source UID relating to the vehicle, VHU, occupant,device any of which may be anonymized and/or may be combined in anyarrangement

For those media sources that broadcast their content through multiplemediums, for example by radio (FM/AM) and internet, these alternativedistribution mediums may be captured and analyzed for their content andassociated timing information, for example using, meta data, audiofingerprinting or other techniques. These streams may be used tocorrelate that certain media events within a location area, for examplea broadcast market, took place and may provide further timinginformation, which can be reconciled with event information receivedfrom a VHU or proxy thereof. The reconciliation of this timinginformation from alternative sources may also be correlated to thereference time ledger to enhance the overall accuracy of the system.This information may be added, for example to an event framework. Theseinformation sets may be used to confirm the sequence of broadcastcontent, in where that information is not provided by a VHU or proxythereof, create a set of events for such VHU based on the time andlocation based correlation of this information set. This may includeusing this information for assigning probabilities to events andinformation sets received from a VHU or proxy thereof. Time correctionmay be undertaken through provision of timing information derived fromthird party providers, such as those that have applications on the oneor more devices of the occupants. This may include those location basedapplications, such as Waze, mapping applications, such as those providedby Google or Apple and the like.

In some embodiments the matching of timing information from VHU eventswith events from other sources, such as location-based smartphoneapplications, may be used to handle cases of missing or incompleteinformation. For example, if the sample events presented in the exampleevents above didn't include location, but there was location informationavailable from a mobile application for the same user who drives thevehicle, it would be possible to match time of VHU and mobileapplication event sets. If matches are found, within a specifictolerance, the locations from the matching mobile application events canbe used as locations for the VHU events. If the match is not exact, itmay be possible to use time difference in combination with speed,location sequences, road network and the like to estimate the locationat the time the VHU events occur. This technique and variations of itcan also be used to extend the reach of VHU events, for example, toinclude driver activities and behaviors that take place before enteringor after leaving the vehicle. This can be used in cases such as solvingad attribution requirements, by finding out the effect of advertisingcampaigns in users, by identifying the users or number of users who goto the advertised places.

Example Embodiment

Each of the time segments recorded in a time ledger, 202-204, as shownin FIG. 2, may have multiple associated events, where such associationmay be bound to a segment in a cryptographically secure form. One ormore events may be configured to be included in an event container,which is written to a blockchain that, in part, incorporates a referencetime ledger 210, 212, 214. Each event container may have a UID 801, asillustrated in FIG. 8.

Each event container may form a part of a reference time ledger 202,203, 204, where the time duration of the container may be quantized tocover a specific period of time. For example, such a period may beconsistent with the content being monitored and any reporting thereof.

An event container, as a repository for event information may containmultiple event information sets 801, as shown in FIG. 8, which may berecorded into an event container at differing time intervals. These sets801 may be identified by the event framework unique identity, which inturn may include timing information that is associated with such eventframework 810. For example, an initial event generated by a VHU or proxythereof may be instantiated into one or more event containers, and at asubsequent time further information sets which are correlated with thatinitial event information set may be added, by reference or embedding,to an event container.

An event container may include at least one of an event start time,duration and/or end time, depending on the time periods of the event803, with such information including the time stamp, time start, andtime stop 830. For example, if a VHU generated event is a change ofradio channel, then this would be recorded as an event with a start andend time that are the same. However, this event may form part of anevent with a longer period, for example if a user switches to anotherchannel to avoid specific content, such as a news bulletin, pledge driveor advertising.

For example, an event container can be written to a blockchain that canbe immediately or subsequently bound through one or more relationshipsto one or more event frameworks and/or the contents thereof 804, and thebinding information 805 associated with such relationship may berecorded, as illustrated in FIG. 8.

FIG. 9 illustrates an event container 901 that combines event frameworksthat include media 902 and vehicle events 903, along with a referencetime 904. Further media sources 920, for example radio program logs,stream logs or similar are then normalized 911, and where appropriatecorrelated 912 to an event framework that includes such media event.This may result in the creation of a further event framework 913 thatincludes the correlated media event. This event framework may then beincluded into an event container 914 that includes the media and vehicleevents. In this manner such an event container may accumulate furthermedia and vehicle events that are associated with the time span of theevent container and may be used in subsequent processing, such as havingassociated causation or other metrics, being part of a report and thelikes

An event framework 1001 can include an organizational structure forevents generated by at least one VHU and/or proxies thereof asillustrated in FIG. 10. In some embodiments, Event Frameworks (EF) 1001,are created when a new event is instantiated, by for example a VHU,1002. An EF 1001 may include: the recording of an event information setby embedding or reference within an EF. Generally, this will beinitiated when an event is generated by a VHU or proxy thereof 1014.Inclusion of at least one timestamp generated by VHU or proxy and/or byevent framework instantiation processing 1012 can additionally beprovided in the event framework 1001. Moreover, a token can be generatedthrough the unique combination of time, location and vehicle identifier1014 and included in the event framework. Further still, the system andmethod can instantiate a sufficiently unique UID 1010 including forexample, authenticated identities of devices, occupants, vehicles (orparts thereof), VHU and the like. Such identities may be anonymized. Theevent framework can additionally include the type of event, for examplemedia, vehicle or other 1014, event container binding information 1024,and sets of event UID for types of events, including media and vehicle1018. The event framework can be hashed so as to create a uniqueimmutable record of event which may include writing to an eventrepository 1024. Moreover, the event framework may be stored locally inVHU and/or in other storage mediums (including cloud/other device) inany arrangement. Such an event framework can be bound to an EventContainer (EC). Such binding may occur at any time subsequent to EFbeing created subject to appropriate EC existing.

Event frameworks may include a single event or sets thereof, for examplean event framework may be initiated when a user selection is made or avehicle or media event occurs, for example when the VHU is turned on andthe volume is set above zero and the audio mute is disengaged. An eventframework can include a set of arbitrary events, for example an eventframework may be stored at any granularity, based on configurations thatmay be implemented as software in a VHU or as processes applied toinformation sets provided by VHU or proxies thereof, including wherethese information sets have been aggregated.

In some embodiments, EF may be written to a repository, includingdistributed ledgers for example, a schema on read database, such asMongo or Crouch, such that these EF may be organized and/or arranged asa further EF for reporting, analysis, evaluation and/or otherprocessing.

As events are generated by one or more sources, for example a VHU orproxy thereof, these events, which in some embodiments may be configuredas Event Frameworks, are aligned and bound with a reference time ledger.In some embodiments, such alignment and binding can be created throughthe use of Event Containers and/or digital fingerprints which arecryptographically bound representations to an immutable time ledger.

For example, as shown in FIG. 8, as each segment of the timeline iswritten to a time reference ledger 804, an Event Container (EC) iscreated with a UID 801 and is bound to that time reference ledger 805,representing a segment of time in such a ledger. Such a segment has astart and end time 830. Each of an EC may provide a structure forsubsequent event inputs (for example media or vehicle). Each of the ECmay be configured so as to have a relationship with one or more eventframeworks 802. This relationship may be cryptographically signed so asto maintain the integrity and audit trail of the occurrence of an eventand any information set comprising such an event.

As described herein, each event may be configured as part of an EventFramework, which includes at least one timestamp from the source of theevent. This initial timestamp may be used to correlate an EF with atleast one EC. This initial relationship of the source time stamp andreference time may have one or more attributes associated with it,including for example, accuracy, confidence, offset or other metrics.For example, if a particular source has a consistent offset compared toreference time, then this may be expressed as a metric. This metric maythen be used in the alignment, correlation and/or matching of furtherevent sources for that EF. For example, a broadcaster log can becorrelated to VHU entry, for example through matching the eventinformation sets and reconciling the time stamps for accuracy.

Matching processes may include keyword, audio or other fingerprint,CRC/Hash or other event attributes and the like. In some embodimentsthere may be multiple sources for a specific event, such as for examplea radio station log and a time aligned stream (for example an internetstream of the radio content). In this example both the time and eventmatching may be undertaken on a probability basis where, for example theminimum error is determined in relation to the reference time.

Individual events may be matched to multiple sources, as many aggregatedsets of events, for example those received from a source that is a proxyfor a group of VHU. Such a source may use a granular time that is largerthan that used in the time reference ledger and as such represents atime based stream of sampled events. In this example, other sources maybe used to refine the time alignment of the events with the referencetime ledger, and may include metrics expressing the degree of certaintyof the time expressed in the EF and the certainty of the relationship ofthat EF to one or more EC. In some embodiments, an event framework mayinitially have a relationship with a specific event container and thenonce further processing has taken place, have that relationship revisedfor a different event container. Such changes may be recorded in arepository, and in some embodiments that repository may be in the formof a distributed ledger so as to provide an immutable record. In someembodiments, such information sets may be stored in a combination ofdistributed ledgers and other databases, for example where arepresentation, which may be cryptographically bound, of an informationset may be stored in the distributed ledger and the information set maybe stored in an applicable database. For example, a unique fingerprintof the information sets, such as a hash, may be stored in thedistributed ledger and the information set from which the hash wascreated stored in a suitable database, for example MongoDB and the like.

Each event framework can be recorded in an event ledger, an eventcontainer and/or an event repository in any arrangement as illustratedin FIG. 11. Event frameworks for media and vehicle events 1101, 1102 canbe bound to an event container 1103 and can be stored, in whole or inpart, in an event repository 1104 and an event ledger 1105. In thismanner, event frameworks 1101, 1102 may be bound to at least one timereference ledger 1121, whilst being stored in other appropriaterepositories. For example, an event repository 1104 may include eventframeworks 1102, 1103 in whole or in part and may have afingerprinting/hashing function 1106 which can create a sufficientlyunique identifier which is then included into an event ledger 1105,which can be a blockchain. In this way the bulk of event information isstored separately form the ledger yet retains the desirable immutableaudit functionality of such a ledger. In some embodiments, an eventledger 1105 and a time reference ledger 1121 may share a commonreference time source 1130. Further such ledgers 1105, 1121 may be boundcryptographically where appropriate. For example, where the eventcontainer includes at least one of a reference time or other normalizedtime span that an event, including sets thereof, an event framework andthe events therein may be bound to an immutable repository, such as ablockchain.

An event framework may be connected, through for example a cryptographicbinding, to multiple event containers, where the event container timespan is less than the duration of the event, or set thereof, which theevent framework includes.

FIG. 12 illustrates an embodiment which combines differing eventframeworks with different event types into a single event container thathas the matching time span for the events therein. FIG. 12 illustratesdiffering event frameworks, for both vehicle 1201, 1202 and media 1203,1204, 1205 events which can be correlated and bound to a set of eventcontainers 1200-1200-(n) representing and bound to trusted timereference ledger 1230. Event containers 1200-1200(n) may of arbitraryduration in any arrangement. A reference time ledger may be bound tomultiple event containers and sets thereof. This binding may include useof fingerprints, hashes and/or cryptographic techniques to ensure thebinding supports auditable chains of control.

Event Configuration and Processing

Information sets generated as part of an event may be normalized so asto create an information repository with certain common characteristics.The process for this normalization may include transformations thatinclude both direct, for example term matching, and indirect, forexample using probabilistic techniques, methods. For example, if theincoming information sets are based on a time granularity that is longerthan that of the underlying repository, for example that in a timereference ledger, the event may be allocated a time that is within thegranularity of the time information and provided with a metric that isbased on the estimated accuracy of the event. This may then be updatedby furthermore accurate information, for example from a contentbroadcaster log, such that the accuracy of the time information isincreased. The methods applied in such a process may bound to such atime event, by reference or embedding.

In some embodiments sets of such time information may be calculatedbased on sample correlations, for example if one or more event hasaccurate time information, for example from a log which is known to beaccurate, from a set of events that are received form the same source,then an offset may be calculated and applied to all the events in thatset. In some other embodiments, contextual information such as, locationand/or weather may be used to correlate with vehicle operations, such aswindshield wipers activation, car thermometer and the like, which mayimprove VHU events timestamp accuracy and consistency. This may createan offset that can be applied to a VHU during a specific period toimprove overall time accuracy. The resulting offset, along withreferences to the contextual information can be recorded on the timereference ledger or other repository with binding to such time referenceledger in order to enable tracking and verification of data analysis andresults.

The use of multiple event information sources may include the use ofmultiple methods for evaluating the correlation of the information sets.As an initial event is expressed in an event framework, this may be usedas a basis for matching to other information sources, including contentproviders, for example radio station logs, streaming services and otherreliable information sources. For example, this may include monitoringthe meta data of an operating streaming service. In such case, forexample, matching the timestamps from the streaming service content playlogs with event attributes, such as selected media source and timestamp,will result in realization of the specific content that was consumed andthe time it was consumed.

Returning to FIG. 7, each event source 701 may provide event datathrough different mechanisms, using different protocols, access methodsand data formats. In order to obtain and use data from multiple sources,a specific connector/extract-transform-load component may need to existfor each event source 710. Other external data sources may also needconnector/ETL components that handle their particular connection 714 anddata transformation needs.

Events and their attributes may come in different forms from differentevent sources, depending on the event information collection method,hardware and software versions, policies, and the like. These eventsundergo normalization, via a module 710, converting them to a form thatcan be applied consistently to all the data processing and analysissteps, including a data processing pipeline 720. For example, an eventsource may report a change of radio station as a series of eventsrepresenting multiple activations of the radio “Scan” button by the userand the final radio frequency selected. This sequence of events may benormalized as a single “VHU_STATION_CHANGE” event. Other eventattributes may need to be converted to a common format (for example,timestamp may be converted from epoch to ISO 8601) or to common units(for example, speed and distance from miles to kilometers or audiovolume level to a normalized 0-100 scale) or to a common referencesystem (for example, geographic location may need to be converted fromthe source geo-reference system to the WGS84 datum).

Event attributes can also be annotated or enhanced by correlatingattributes with external data sets. For example, location with marketgeo-locations in order to assign specific market and time zone, radioband and frequency with radio station names per market or satellitechannel number with satellite radio channel lists, in order to assign aspecific broadcast media source to the event.

The events in the normalized and/or annotated format can then be storedin an event information sets repository 730. The time information forthe event information sets can be recorded on a time reference timeimmutable distributed ledger 740 and their unique identifiers, as wellas their personal identifiable information (PII) can be recorded on anidentity distributed ledger 742 and on access-protected portions of theevent information repository.

The sets of normalized and annotated events and the external data canthen be used for processing, matching and analysis via module 720, whichmay include identification of experience sessions, such as start and endof listening sessions to a specific station, source or content piece(such as a song or a radio program), classification of the vehicleoccupant based on location, vehicle make and model, experience behavior,etc. (or combinations of them), identification of behavioral patterns(movement, listening, driving and combinations of them), and generationof data aggregations 750 and measurements for sets of occupants by area,demographics, time and other data classification criteria.

The following example shows a set of listening experience sessionsobtained from the example events presented before and formatted in JSON:

“listeningSessions”:[{ “sessionId”: “D6911D8F18FE416EBD9C032E85227DB2”,“anonymousCarId”: “XY0677052M92”, “MSA”: “Los Angeles”,“startTimestamp”: “2018-08-31T06:05:25Z”, “endTimestamp”:“2018-08-31T07:06:30Z”, “attributes”:{  “source”:“CD” } }, {“sessionId”: “98C57C48DE1C4CF4864B630FEFFE2D9C”, “anonymousCarId”:“XY0677052M92”, “MSA”: “Los Angeles”, “startTimestamp”:“2018-08-31T07:06:30Z”, “endTimestamp”: “2018-08-31T08:36:33Z”,“attributes”:{  “source”:“Tuner”,  “band”:“FM”,  “station”:“KPCC-FM” }}, { “sessionId”: “6CACDD02BEB84ECF9BE2427C9331F592”, “anonymousCarId”:“XY0677052M92”, “MSA”: “Los Angeles”, “startTimestamp”:“2018-08-31T08:36:33Z”, “endTimestamp”: “2018-08-31T09:07:30Z”,“attributes”:{  “source”:“Tuner”,  “band”:“FM”,  “station”:“KVCR-FM” }

Individual behaviors and behavioral patterns, as well as aggregated onesand their correlations with external data can be identified, classified,and stored, via a matching module 722, as learned data, historicalreference data or individual user profile data 752, which can be used asexternal or contextual data sets for processing future events and/or asan output product of the one or more processes.

In some embodiments, events may be normalized to conform with one ormore event format, such normalization may include the followingoperations:

Time stamp reconciliation: time stamp may be normalized to referencetime ledger and may have other offsets applied when further eventcorrelation information, such as for example other information sourcesfor the same event. This may include those that are determined torepresent the event through probability or other statistical techniques.

Event and/or event information sets extraction: this may includesegmenting a file or other data repository into individual eventsrepresenting a sequence of events related to a time line. A system mayalso be configured to identify and extract specific information fromeither the repository or a sets of events, some of which may be part ofsuch repository.

Event correlation: this may include analysis of an event information setand selection of at least one element for comparison and correlationswith other events.

Event quantization: A specific time period, such as that typically usedin an advertisement break in a program, may be used to quantize a set ofevents so as to configure these for other operations, such as reportgeneration, pattern matching and the like.

Certainty: A set of metrics may be applied to an event or part thereofto identify the degree to which the event information is accurate. Thismay include the identification of the methods employed to evaluate suchmetrics.

The collection and collation of events bound to one or more timereferences, provides a basis for detailed evaluation and expression inthe form of one or more metrics. Some of the metrics are currently inuse within the media industry; however, the accuracy and validity ofthese has to date been largely based on statistical sampling with littlecorrelation and a variable time reference of limited accuracy.

The instant system and method improve upon prior usage of metrics withthe reliance on an accurate bound time reference that is recorded as ablockchain, with each event set being referenced to that time line andcomprising, for example, multiple correlated event instances. Theaccuracy and authenticity of the events, as such, becomes reliable andtrustworthy to the degree that behavioral metrics and other patternbased evaluations become relevant and meaningful. Metrics may beconsidered in a number of categories; however, the reliable immutableunderlying event and time information may support any combination in anyarrangement, the following of which are indicative examples.

Event metrics may be configured to match common representations used bycontent providers and/or advertisers. For example, metrics presented toadvertisers may include:

1) Exposure

2) Conversion

3) Attribution

Such metrics can be tied to a reference time ledger, providing proof ofperformance of the material. In the case of content, for example radioprogramming, music or other content, the events metrics may include forexample:

Time of content start;

Time of content stop (through either change of content or listeningvolume set to zero or muted);

Changes in volume (and any peripheral events, such as incoming phonecalls); and

Tagging or marking of any content.

All of above metrics can be tied to a time reference ledger, providingan accurate immutable time at which such events occurred. These eventsmay be aggregated as sets for a specific piece of content, for example aparticular song may have a number of such events aggregated acrossmultiple users, and as such provide a detailed perspective on userbehavior for that song, for example, many people increase the volume atthe chorus, or how many people change channel or lower volume at theverse or similar.

The creation of such event metrics where multiple sources of informationare correlated and bound to a time reference ledger provides an accurateand immutable record of user behaviors. Further event metrics may becreated through the use of techniques, such as for example:

inference;

calculation;

pattern recognition (Patterns and Anti-Patterns);

probability; or

the like.

In a number of embodiments, machine learning methods may be applied tosets of events, and the information there in, for example to createprobability distributions for events, in whole or in part, or thelikelihood thereof. This may include such techniques as clustering,classification, regression and role extraction in any arrangement.

The combination of accurate time and event granularity provides uniqueopportunities for predictive metrics. For example, the event informationsets that are received from a broadcast station program list, may beused to evaluate the information sets received from a VHU to establishcorrelation of the time and content elements of the information sets anddetermine the probability distribution most relevant to the informationset under evaluation. The correlation between the time granularity usedby the VHU, or provided by a proxy thereof, and that provided by theprogram listing may use matrix factorization to establish clustering andclassification outcomes.

In some embodiments these metrics may include one or more reference(s)to method(s) used in their creation. These may be presented asinformation sets that may be further processed by systems designed tocreate additional metrics, reports or similar. The methods deployed andtheir application to specific information sets may be recorded in anappropriate distributed ledger. This may include the use of a repositoryfor such methods, which is cryptographically linked to a distributedledger, for example where a hash of the method, the time, the results ofthe method and the target of the method is stored in the distributedledger. In some embodiments, these elements may be processed in a secureenvironment, such that the processes undertaken and the outcomes thereofare encrypted. This may involve the use of a secure key managementrepository which may be connected to a distributed ledger, for exampleto store a hash of the key or the key itself. In this example, thesecure repository may hold the encryption key with a key managementsystem providing the capabilities to manage and enforce permissions forthose keys. The distributed ledger may record a hash of the key, andpotentially other information held in the connected repository as animmutable record. This information, subject to appropriate permissionsmay be used to validate the metrics and methods employed in support ofthe content monitoring and measurement, for example by multiple contentproviders, including advertisers and the like.

This approach provides provenance of “insights” that may be createdthrough such an analysis where for example, each outcome can be tracedback to a specific set of information, expressed as, for example, anevent framework bound to a time reference ledger, that immutablyrepresents the event occurring, and the methods employed in the creationof any metrics associated with the event.

One advantage of such an approach is the ability to create patternsbased on real time events with accurate, reliable and verifiable timinginformation. The underlying event and time information sets are bound tothe immutable one or more reference time ledgers 202, 203, 204, as shownin FIG. 2, to provide a basis to create metric sets that support, verifyand quantify both exposure and causation of actions, at least in part,resulting from such exposure.

Some embodiments may employ spatiotemporal databases as repositories forsuch information sets. This database may be connected to one or moredistributed ledgers, including time and location reference ledgers thatprovide an immutable record of events stored in such databases. In thismanner, a verifiable record of when and where exposure to contentoccurred may be used to evaluate vehicle or occupant behaviors inresponse to those exposures.

Behaviors may be, in some embodiments, characterized as journeys,sessions and patterns. For example, a journey may comprise aninitialization sequence, location variations and a completion sequenceall of which are correlated to a time reference ledger. Such a journey,which may be represented as a set which includes the following events:

journey start;

journey location waypoints;

journey completion; and/or

journey regularity.

The segmentation and granularity of a set of events comprising such ajourney may in-part depend on uniqueness of journey, for example, if ajourney location way points are unrecognized from previous journeys thengranularity is high, for example each street change, whereas if journeylocations are recognized then journey granularity may be reduced to aminimal information set, such as each waypoint. This granularity may beundertaken with both time segmentation and/or location segmentation.Journeys may include sets of nodes, e.g. waypoints. An observed node orwaypoint may be associated with an unobserved waypoint, such thatpatterns may be determined where minor variations in a journey arereduced in importance or not considered, unless they are associated witha point of interest (POI), such as a retail location or similar.Techniques such as affinity graphs may be used for this purpose. Suchgraphs may then be configured to create representations that arecorrelated to content consumed within the time segment to identifybehaviors associated with that content.

The journey events may be determined from the vehicle events that arecaptured in, for example an event framework bound to, at least one,reference time ledger. As journeys are completed, patterns of behaviorcan emerge. For example, taking children to school or undertaking acommute to work and as such, when deviations from these journey normsoccur this may create an exception which may trigger greater granularityof information capture or other actions.

For example, vehicle location information may be determined through theevaluation of the start timing and initial location and the end timing(for example when the vehicle is turned off), where the intermediatelocations are unknown, based on a geographical map of the areatraversed, the relative timing of such a journey, providing aprobability analysis of the route most likely taken. For example,tensors may be used to represent the relationships between time, roadsegments and vehicles, in the form of a three dimensional tensor.Further, for each content broadcast in a location at a time, anothertensor may be built that represents the average response to that contentat that time for that location. This supports the evaluation of eventstreams received from vehicles to determine their relationship to thisaverage tensor. For example, if there is an anticipated response to aspecific content element, for example based on the number of exposures,a deviation from this may trigger an exception, requiring a moredetailed review of an event stream. For example, an event informationset can include or be correlated to a navigational information providedby a VHU, or vehicle system, or a device that is in the vehicle capableof producing such information, such as a cell phone, where thatinformation can be recorded as part of the event set, this can be usedto further identify the route of a vehicle.

Sessions may include sets of events that represent a user's interactionswith vehicle media and/or communications systems, such as those involvedin, listening, viewing, interacting, transacting, including a VHU. Forexample, this may include sources such as in car entertainment,communications, and other devices co-located in the vehicle at the timeof the session. A session, for example, may have a start and end times,which are recorded in, at least one, reference time ledger.

In one exemplary method, the representation of session information setscan make use of acyclic graphs, where each event is a node on areference time line. In some embodiments, this time line may be relatedto the session and may be adjusted to comply with a reference timeledger which is tied to the systems reference time source. In thismanner a time that is the source of truth can be configured.

Some of the event information that may comprise a session can include,for example:

-   -   the sources of the media;    -   the durations of the event;    -   the volume or other audio control functions;    -   any “edge” points where a user interaction occurs (e.g. Volume        up/down; channel change; phone interrupt etc.); and/or    -   the like.

The recording and storage of events and their timing in an immutablerepository can provide a source of truth that may be relied upon. Such arepository may be used for multiple sets of metrics that may be createdthrough the use of one or more probabilistic techniques, including thoseof machine learning, deep learning and the like. In some embodiments,these metrics may include, identification, aggregation, correlation,causation, classification and the like.

Metrics based on probability analysis, including probability domains,may include:

causal relationship metrics;

journey deviation metrics;

experience variance metrics;

accumulation metrics;

exception likelihood metrics; and

the like.

Each of the aforementioned metrics may be created through analysis ofmedia and vehicle events, based on a common reference time ledger toascertain the relative relationships and their correlation to thepatterns of behavior that have been determined over the course of astatistically significant set of previous events.

In some embodiments, the use of multiple granularities of time, avehicle or the VHU may provide time accuracy in seconds for an event,whereas a pattern of events, locations and content may become apparentacross a time period of weeks or longer. Such an information set may beevaluated using Recurrent Neural Networks (RNN), for example with theLSTM (Long Short Term memory) approach. Further techniques such as gatedRNN' s may be used, so as to separate the activities that resulted fromexposure to content from the occasions where there was no such activity,within the context of an accumulation model recognizing each occurrenceof the content.

The extraction of rules from data sets can involve establishing theconditional probability of the time period, content identification,location (including relationships to POI that are associated with, orhave a significant relationship with, the content), routes that incombination form journeys to such locations and associated identities.This may be used to establish rule sets that are applied to any of theseentities in any combination. These entities can become thediscriminating features for the data sets being evaluated.

As the information sets that are operated upon may come from differentsources over a period of time, a range of accuracy validations may beapplied, such as mean absolute error, mean squared error, true and falsepositives, as well as traditional precision and recall metrics. Thesesrange of accuracy validations may all form part of the coefficients ofvariation that may be applied to the clusters that are analyzed by thesystem.

The application of reinforcement learning techniques based onstate-action pairs may also be applied when a state has been recognized,for example a regular route and associated behavior patternsestablished, for those situations where an action that diverges fromthis pattern may be used to create a model with the appropriate rewards,that can be applied to further large data sets. This includes creationof tensor or other representations of information sets that may beencoded or be derived from state-action pairs or other discriminatingfeatures in any combination. This provides a high degree of flexibilityin analytic composition, though combinations of features, to aid indetermining the behaviors of content providers and content consumerswithin the context of a vehicle.

Within the determination of state, the use of location segmentation maybe applied, including treating a regular route a vehicle takes as alocation segment. This can effectively reduce the number of data pointsto be considered in any evaluations, providing a “clipping” function soas to avoid parameter gradients. The activity within such a locationsegment may be the hidden layer.

Identification

As noted above, in some embodiments the details of an individualoccupant or a specific vehicle may be masked or obscured so as toprevent direct identification of either of these. Some information setsmay be aggregated such that the event and vehicle information setsrepresent groups or classes of vehicle and/or occupants. In someembodiments, there may be sufficient information available to ascertaina sufficiently unique identifier for a set of vehicle and/or mediaevents whilst still preventing the accurate identification of the actualoccupant or specific vehicle, meeting personal identificationinformation (PII) obligations where appropriate. There may also besituations where an occupant opts in to provide a sufficiently uniqueidentifier for themselves and/or their vehicle.

The system may be based on sufficiently unique identifiers so that eachset of media and/or vehicle events may be bound to such an identity withan associated measure of certainty so as to create patterns of behaviorthat may be used for further evaluation and processing, includingcorrelation and causation analytics. Such identities may be stored in adistributed ledger, databases or other repositories in any arrangement.

In some embodiments, a unique identification (UID) may be incorporatedinto a device, such as a VHU, vehicle systems and or other device, suchthat these UID may be verified as authentic presentations of suchdevices, for example through reference to a trusted repository of theseidentities. In this manner the source of events may be authenticated tospecific devices, including groups thereof, through in some embodimentssuch identity may be obscured in manner that makes it computationallyimpossible to identify personal identifiable information of the deviceand any associated users.

Some of the event information sources may include event information setsthat are partial in nature. For example, a file of event information mayhave been corrupted or disrupted such that the events themselves areincomplete, such as only having a single time stamp, which is notidentified as start or end, having little or no other event informationsets or comprising multiple events that have been merged without anyindication of that occurring. In this case these events are consideredas partial events and may be identified as such and consequently theprocessing for normalization and integration with the rest of thesystems information systems may differ. This may include usingtechniques associated with sparse data analytics and other suitabletechniques so as to, as far as is practical rebuild these eventinformation sets into as complete as possible versions. This may includecorrelation with other event sources with sufficient common elements,using for example, probability distribution techniques, and othermatching, based for example of time, keywords, content or otherfingerprints and the like.

These partial event information sets may be identified as such, as maymetrics bound to them indicating this state and the potentiallyreferring to the methods employed to recreate or rebuild the informationsets of those events.

Patterns may comprise sets of individual events, including media and/orvehicle, and the information sets thereof correlated to a reference timeledger. Patterns may be determined for journeys, sessions, experiences,locations, content and other system elements in any arrangement. Each ofthese patterns may be represented by a number of differingimplementations, including but not limited to acyclic graphs, or othergraph based representations, lattices, multi-dimensional spaces,manifolds and/or other information management and repositoryembodiments.

A pattern may include any set of information, where the relationshipsbetween each information element in such a pattern are declared orderived. Frameworks for patterns may be employed to organize suchinformation sets, for example a pattern for a journey may include forexample:

the journey start—VHU provided information of vehicle start and movementinitiation;

waypoints—sets of locations that a vehicle may transit as part of ajourney; and/or

journey end—VHU provided information at the conclusion including vehiclestop.

For example, such a pattern may be a road map sequence representing ajourney with associated time information for each portion of such ajourney. A waypoint may comprise a location or point of interest (POI)that a vehicle passes and may also include timing information regardingthat vehicles transit of such location, including whether the vehiclestops for an extended period, that is a period longer than thatassociated with a traffic light, stop sign or other road marking or forexample if a vehicle is at a POI which is a school or drive throughfacility. For example, a morning commute may include dropping childrenat a school or transit location. Waypoints may also be configured toinclude situations, such refueling a vehicle where the engine isswitched off, though the media systems are still operational and anoccupant may have left the vehicle. This level of granularity of mediamonitoring improves the reach and understanding of occupant behavior.

Patterns may include vehicle and media events and combinations thereof.For example, there may be a repeated journey, such as a commute, whichforms a pattern based on this journey to which media experiences may beadded to create a behavioral pattern for the occupants of that vehicle.Pattern determination can be focused on individual listeners/driversand/or on groups, where grouping criteria can be based, for example onmarket area, type of vehicle, demographic classification such as agegroup, and the like.

In some embodiments, repeated patterns (such as common sequences ofvehicle events, media events and other behavior indicators) may beidentified, and for example classified. These common patterns can becorrelated with contextual information, such as weather, traffic, time,calendar events and so on.

Repeated patterns can be of intrinsic value and may also be used as abaseline for identifying unique variations, such as cases when commonpatterns are expected but are not occurring. In such cases processes maybe initiated to correlate these exceptions with other events andcontextual information, such that a causation analysis can be performedin order to identify triggers, such as weather variations, exceptionaltraffic, fires or other disruptions or causes the variations in thesepatterns.

For example, a sequence of locations that frequently match a route onthe city street map during a specific time range every week day can beidentified as a common commute route for an individual vehicle, whichmay be expressed as a journey. A specific audio or other source may bematched with the commute route pattern, adding to the “commute behavior”pattern for the vehicle occupant(s). Cases when the common route or thecommon audio source don't match the commute behavior pattern can beidentified and analyzed, correlating, for example, with trafficinformation, weather, local events. This can result, for example, infinding common route deviations that tend to occur on rainy days.

As a journey unfolds, a VHU or proxy thereof may provide event streams,comprising both vehicle events and media events, forming sessions. Thesestreams provide sequences of events that when correlated to the journey,in terms of locations, vehicle operations and time, provide a uniqueinsight into the behaviors of the vehicle occupants. These may formpatterns that through repeated behavior, provide insights into therelationship of the occupants to the media, within the context of thejourney.

Aggregation

Aggregations of these information sets may include establishing thefeature sets that are pertinent to both the use of such techniques asmachine learning as well as those feature sets used by content providersand distributors. Establishing patterns of types and distribution ofcontent, locations and their relationships to vehicles, occupants andtheir selected experiences and vehicle characteristics, all related toan accurate time and immutable record of these undertakings creates aunique capability to establish effective metrics reflecting insightsthat are valuable to all the stakeholders involved.

Aggregations may include, for example, counts, such as number of uniquemedia consumers, number of consumption session starts, and number ofvehicles with active sessions. The aggregations may also, oralternatively, include calculated values such as average consumptionsession length. These aggregated values may be calculated and groupedfor every media source, for every program, content piece or advertisingpiece, for every specific period of time, such as quarter hours, forevery market area and for every demographic, among other groupingcriteria.

One possible direct application of these aggregates is in the form ofdata visualization dashboards that stakeholders can use to select groupsand filters, such as time periods, locations or markets and sources, inorder to obtain insights, in graphic or tabular form, that are of valuefor their specific interests. Some examples include vehicle OEMs whowant to identify media sources most used by their customers, grouped byvehicle make and model, or media content providers who want to measurethe effects of content changes in their audiences, or advertisers whowant to identify the time periods, media sources and markets where theirads will be most effective.

Correlation

Correlation may be used to identify and evaluate typical and atypicalevents through comparison of journey information and experience orsession information, resulting in part from vehicle event streams andmedia event streams. One aspect of this is the calculation of the timedifferentials from one or more exposures to media content, differencesin such content, including personalization, localization or othertargeted content variations, and changes in the patterns of behaviors ofvehicles and their occupants.

The evaluation of event information to establish correlations, usingtime, location, vehicle characteristics, user experience variations,contextual information and other sources may be undertaken throughconfiguring one or more machine learning techniques, including forexample those employing probability distributions. In some embodiments,these techniques may include the use of Hilbert spaces and/or othermulti-dimensional spaces to align events, their context and theiraccurate timing.

Causation

A key aspect of the system is establishing the relationship between thecontent occupants of a vehicle experience and the impact of thatexperience on their behavior. As the system may comprehensively monitorthe experiences of occupants in vehicles and their typical patterns ofbehavior, the potential is created to establish relationships betweenthese experiences and occupant's behavior.

These relationships can encompass not only the vehicle occupant'sresponses to, for example, advertisements in broadcast material, such asradio, but also their adjustments to the VHU entertainment controls, forexample raising or lowering the volume on a specific musical piece ortype of music.

The information generated by these actions, especially when such actionsare atypical in relation to established patterns of behavior, provide abasis for establishing causation between a media event and a vehicleevent. For example, if an occupant directs a vehicle to a recentlyadvertised location, for example one offering a significant discount toa product and that direction is atypical in relation to the typicalpattern of behavior of that occupant at that time in that vehicle, thena causation metric may be set, for example using an exception toundertake a process of evaluating the event information and patterninformation to determine a causation metric. This may then berepresented in one or more reports and/or may trigger other evaluationsand/or processes.

The use of vehicle event patterns and media event patterns to expressbehavioral characteristics, when combined, provide patterns for journeyswhich may be used as a basis for determining atypical behaviors andcorresponding causal relationships. Once a causal relationship isestablished, which may be done in real time or near real time, this maybe used as a trigger for other events or actions, such as the creationand presentation of offers to vehicles occupants prior to their arrivalat, or at their destination, for example when they have deviated fromtheir typical route to take advantage from an advertised offer.

Such offers may also, subject to a user opting in to do so, be deliveredto a device that the occupant controls or has access to, such as acommunications device, VHU, Internet of Thing (IOT) device or otherdevice capable of receiving such an offer. For example, a barcode orsimilar may be sent to an occupant's smart phone in response to theircausal metrics exceeding a threshold and the vehicle events confirmingtheir change in route to a destination associated with the offer.

The matching between events and contextual information (weather, schoolschedules, portions of the day—such as morning drive—, traffic, localevent calendar, etc.), used in the different pattern identification andclassification methods, is based on location and time. Since the timereferences, time matching among events and between events and contextualinformation, and the time corrections are all stored in an immutabledistributed ledger (such as a blockchain), the patterns andclassifications can be backtracked, verified and validated, which addstrust and certifiability to the process' end results.

Classifiers

In some embodiments, the system may include a classification system1502, as shown generally in FIG. 15, which in some embodiments may bedeployed as ontologies and/or taxonomies. These may be used for theevaluation and processing of information sets of both media events 1510and vehicle events, and parts thereof. Classifiers may be used tocategorize journey types 1512 and patterns of behavior 1520 so as tosupport evaluation of the large scale information sets generated in thecourse, for example, of the morning or evening commute of a major city.

One form of classifier 1502 may be based on patterns that are determinedthrough evaluation of media and vehicle events that are immutably bound,directly or indirectly to at least one reference time ledger. Forexample, this may include classifying journeys intro classes such as;morning commute, evening commute, shopping, family visit, sport, andother common journeys, shown in block 1512. There may be furthersegmentation into sub-classes by time of day, week or month and thelike. Some embodiments may include matrix relationships 1520 basedaround journeys, time and events.

As illustrated in FIG. 15, properties from event set(s) (such as vehicleID, time, location and activity) can be matched with the properties fromexternal datasets (such as content programming lists, demographics basedon market, media consumption and vehicle ownership, weather, holidaycalendar, traffic, etc.). This matching can be based on time and/orlocation with different levels of granularity. Algorithmic or derivedclassifiers 1502 (such as neural networks) can be used to producedetailed classifications of, for example, demographics, mediaactivities, and drive journeys.

Relationships between classes can also be established based on location,time and other attributes. For example, media activities can beassociated with demographic types and drive journey types around alocation of interest, such as a store, which can be used by the storebusiness to make informed marketing plans and ad purchase decisions,shown as matrix 1520. In the illustrated example, a matrix is formedbased upon driver demographics, e.g. college educated female, collegeeducated male, non-college educated male, etc., the journey types, e.g.morning or evening commute, or a weekend getaway, and the particularmedia activity types, e.g. listening to one of news, pop music, orsports, as a function of proximity to a particular targeted store.

Classifiers may have multiple dimensions based on patterns, such as forexample, including elements such as time, journey, session andidentities, where each of these may form part of an array which formsthe classifier. The classification may be fuzzy in that not all of theseelements are present, or the attributes of an element includesprobability metrics, that although they are within a defineddistribution, for example within a percentage deviation, provideinsufficient certainty for that element. Classifiers may also includeelements and sequence information, where the sequence of events maydetermine the classification of certain journey types.

For example, a point of sale (POS), e.g. a retailer, for a product thatis being advertised, may be used to classify those journeys that passwithin a specified radius of that POS, where the time and location ofexposure to the that advertisement is recorded. This information mayalso inform a content provider when an advertisement should bedelivered, such that the impact in relation to the location ismaximized.

The foregoing includes classifiers that are determined by metrics thatare commonly used by advertisers, broadcasters, content providers andothers as well as those classifiers created through analysis of theinformation sets in aggregate through such techniques as machinelearning, deep learning, spatiotemporal data analytics and the like.

Information Integration

In one embodiment, an aspect of the system can be the collation andmanagement of sources of information into a cogent and accuraterepresentation of events and their occurrence in relation to a referencetime recorded in an immutable manner, such as with a blockchain.

FIG. 7 illustrates one exemplary embodiment, the elements of which aredescribed below.

The illustrated system elements, of FIG. 7 for example, perform thefunctions and processes for extracting, transforming and/or configuringinput source events to a normalized format suitable for subsequentprocessing. Such functions may include synthesizing information sets,and retaining identification thereof, where the event information isincomplete or sparse, for example where the event data is presented insample form. As shown in FIG. 7, each of the connector/ETLs 711, 712,714 is configured for a type of event input, where the input eventinformation set may differ from connector to connector. As the output ofeach connector/ETL 711-713 can be consistent to the format of the eventframework (EF), there may be an additional process which can evaluatethe incoming event sources to configure each Connector/ETL 711-713. Thisprocessing may be undertaken prior to or at the time of connection andmay involve one or more processing steps performed by an in-vehicle headunit 701.

In some embodiments, one or more Identity Ledgers (“ID Ledgers”) 742 canprovide a repository for sets of personal identifiable information (PII)of individuals and vehicles which is stored in an immutable, encryptedform using a distributed ledger, such as a blockchain. The uniqueidentifier (usually a cryptographic hash) of every entry in the IDledger can be used to anonymously reference the individual or vehiclealong data processing pipeline. The PII information within the ID ledgerentries can be partitioned and encrypted in order to provide controlledmulti-level access of such information to different actors.

This may include for example PII for each of an occupant within avehicle, where such information set is available. In some embodiments,there may an encrypted repository that stores PII, which is external tothe blockchain, though it is cryptographically bound to that blockchain.In one example embodiment, the blockchain may be store the public key ofthe external to the blockchain encrypted repository of PII, such thatthis information may be verified through use of this public key. Thecombination of blockchain and external encrypted repository may be inany arrangement, including of the relevant keys for such encryption.

In some embodiments, each event container (EC) 740, as shown in FIG. 7,or a unique fingerprint thereof, can be associated to specific timesegment in a reference time line based on at least one timestampincluded in such EC 740. This relationship can be stored in an immutabledistributed ledger, such as a blockchain. A reference to thisevent/event container/time slot tuple may be used within one or moreanalytics processes modules 720, which enables functions such asverification, validation and/or tracing or backtracking of measurementsand/or analytics results sets.

The reference time line can be implemented as a distributed ledger, suchas a blockchain as shown in FIG. 12, where each block or record storedwithin represents a time segment or a timeslot in the trusted referencetime ledger 1230. As a result, every timeslot in the time line 1230 canhave a unique reference (cryptographic hash) which cannot be modified.This unique timeslot reference can then be used to associate it with anyEC with timestamp that matches the timeslot 1200-1200-(n). Thisassociation may be stored in one or more distributed ledgers.

Reference time ledgers may also be used to store time information forprocessing and analytics, ingestion of external data sources,aggregations and integrations and other system functionalities.

Event Information Sets Repository

An event repository comprises sets of normalized events, which are theoutputs of one or more connector/ETL 711-713, FIG. 7, coming frommultiple event sources that are stored, along with their respectivereferences in at least one ID ledger and at least one reference timeledger, in one or more event data repositories 730. Such repositories730 may be subsequently updated with further event information fromexternal data sources 760, where for example processing and analyticshas sufficiently established that such information sets correlates tothe such stored event information sets. This correlation may includeprobability analysis, which for example may be represented as aconfidence metric for such information. In some embodiments, such storedevent records can be used for subsequent processing, data matching,analytics and/or aggregation processes.

There may be multiple external data sources 760 that are coupled to thesystem so as to provide and support accurate event information sets,including timing of such events and can be stored in an external datasource repository 724. In some embodiments this may include three typesof external data sources 762, 763, 764.

In some embodiments, one of the external data sources 760 can include areference data sources 762, which itself may include: geographic andother location data, for example, GPS or similar, ZIP codes, city,suburb, metro area, political or other governmental areas andboundaries, utility designated areas, state or industry specific marketstatistical area and the like; and/or broadcast information including,Radio and or TV band and frequency information, satellite radio channelidentifiers, wi-fi or other communication channel information and thelike.

In some embodiments, the external data sources 760 can include thirdparty data sources 763 can include any number of additional dataresources, including: landmarks, points of interest, business locations,map, road networks, police, fire or other utilities, commercial andother locations and/or specifically identified locations that may be ofinterest, for example a specific advertiser location or set thereof.Other data sources may include, for example, demographics, based, forexample, on location/market, vehicle type and/or listening patterns andmay further include other contextual information sets such asweather/traffic patterns/holidays/local events/news and the like.

In some embodiments, the external data sources 760 can include sets ofevent centric data sources 764 including information that aresupplemental to and/or validation of one or more sets of events, forexample radio station logs/streamed internet radio and other proof orperformance information sets, including sampling and evaluation of suchsamples, for example using fingerprinting or other recognitiontechnologies, Advertising play plans and logs and the like. In the casewhere the individual is identified sufficiently, such information setsmay include event information sets generated through their use of rentalcars, ride share services, autonomous vehicles or other temporarytransport they may use.

An events' geographic location can be matched with a market areasgeographic boundaries in order to assign a market area to every event byway of a processing, matching and analysis. For example, a market areamay be a characteristic of an event or set thereof. Market areas may bethose used currently by broadcasters to define their addressable marketsegment and/or may be those defined by usage patterns of vehicleoccupants as they undertake their journeys. These matchedcharacteristics can be undertaken with multiple levels of granularityfor each market area (for example, country, state, metro area, city,postal code, industry-specific statistical area and/or usage definedarea).

For example, when active media source is radio, an event's band,frequency and associated market area information can be matched withradio station data in order to obtain a specific station identification(callsign) and other station data such as format, programming and playlogs. This information may include various time stamps which may then bealigned with the time reference ledger and in some embodiments mayprovide further event information for an event. Such informinginformation may be used to validate or further increase confidence thatan event generated by a VHU or proxy was an accurate representation ofthe occurrence of such event and that the occupant of the vehicle inwhich VHU was situated were exposed to and experienced such an event.

The matching of geographic information sets to event sets, by way of theprocessing, matching, & analytics module 720 may include, for example:

Events' geographic location being matched with road network data,landmarks and/or point of interest data in order to provide in whole orin part context for analysis of travel and behavioral patterns.

Events' geographic location and time information being matched withlocation and time dependent contextual data, such as local events,weather and traffic and the like.

Locations from consecutive and/or sequential events belonging to thesame vehicle being matched with one or more location sequences fromother sources, such as cellphone location logs, so as to extend andexpand scope and/or depth of movement pattern analysis.

This approach supports further matching with demographics and otherlocation-specific data to be undertaken. For example, an events' vehiclemodel, make, year, vehicle type and other vehicle characteristics can bematched with demographic data sets in order to associate the events withlikely demographic profiles. The event centric data sources 764, wheresuch information may be ingested through an appropriate connector 714and stored in a repository 740, which may then be queried by a matchingsystem 722 forming part of the processing, matching and/or analyticsmodule 720. In some embodiments this may involve one or more machinelearning types of techniques and/or other probability based techniquesto establish one or more metrics for expressing the confidence,certainty, accuracy, timeliness or other attributes of thisrelationship.

Aggregations and Integrations

Establishing patterns in the information sets stored in a distributedledger 107, as shown in FIG. 1, can be undertaken by sets of processesin a data analytics pipeline 105 which when configured appropriatelyproduces outcomes, such as aggregations, measurements and insights whichcan be stored in a repository 106.

The data analytics pipeline 105 may comprise a set of machine learningand similar analysis tools, organization tools and systemizations andprobability domain based tools. Each set of these tools may beconfigured to operate independently, in an arrangement, for example,sequentially or in parallel, and/or recursively in any arrangement.

Machine learning tools may include, for example, supervised andunsupervised learning techniques and variations thereof, with thosetools most suited to pattern recognition of temporally based eventstaking precedence. For example, the use of kernel Hilbert spaces andRiemann manifolds, and other topological approaches, may be deployed toestablish and create suitable ontologies and taxonomies for patterndefinition and information organization. Such an approach can benefitfrom an enhanced mechanism to determine the relative quality of sets ofprobability domain information that are temporally connected, though notnecessarily is a sequential or continuous manner. This may also includeevaluations prioritizing movement and location, for multiple timeperiods, and may involve the use of Cartesian, fuzzy and latticeapproaches.

Patterns that are determined, in part, by such techniques, may then bearranged according to one or more organizational methods, such asontologies and taxonomies. These organizations may be used as featuresets to which information sets held the distributed ledgers arecorrelated and may be employed for evaluation of third party informationsets to be correlated with event information sets held in such ledgers.

Many of the information sources, including those held in the ledgersand/or repositories bound to those ledgers, and those incorporatedthrough third party services, may be subjected to standard probabilitydomain techniques to establish clusters and other informing features.These may include principal component analysis (PCA), formal conceptanalysis, support vector machine (SVM), kernel techniques and othercommon machine learning approaches. The use of matrix and latticetechniques for the evaluation and expression of information sets so asto establish and manage patterns for event monitoring can also beincorporated.

Given that the information sets upon which such machine learningtechniques can be applied are constrained to those events generatedthrough a VHU or a proxy thereof, other information sources relating tothose events and the context in which those events occurred, the rangeand type of machine learning so applied provides specific advantage inthe determination of patterns that include these events are well suitedto a topological approach. The use of an immutable reference time tointermediate and/or weight probability distributions supports methodsfor enhancing the reconciliation and in some cases establishing causalrelationships. The following is an example description of a commonpattern, based on the previously discussed example events, which may beidentified using one of the described techniques, applied to events fromthe same vehicle in previous days and weeks. The vehicle occupant tendsto take a similar route during weekday morning drives (from around 7:00AM to around 9:00 AM).

{ “patternId”: “C7D7690D8E1F1FE0164B630A34B8323A”, “anonymousCarId”:“XY0677052M92”, “MSA”: “Los Angeles”, “daypart”: “Morning Drive”,“dayOfWeek”: “Weekdays” “startTime”: “07:00”, “endTime”: “09:10”,“travel”:{  “from”:{ “zipcode”:91101  },  “to”:{ “zipcode”:91773  }, },“listen”:[{  “source”:“Tuner”,  “band”:“FM”,  “station”:“KPCC-FM” }] }

The specific events in the above example show an exception in the commonpattern: at 8:36:33 local time the radio station was changed to KVCR-FM.Contextual information shows that KPCC-FM, the NPR radio station thevehicle occupant usually listens to, started a quarterly pledge drive at8:30. Long-term historical events from the same vehicle (or events frommultiple vehicles in the same area and equivalent demographic group) mayshow a correlation between the station pledge drives and change innumber of listeners.

In some embodiments, patterns may be created that represent what arewell understood behavior patterns for users and vehicles. These mayinclude:

Morning/evening commute;

Morning/afternoon school run;

Regular journeys (for example with common destinations such as forshopping/social/work);

Vacations;

And the like.

Each of these categories may be applied as an identifier for a pattern,as may time of day, day of week and week of year temporal segmentations.Holidays, school events and other calendar based social information setsas well as industry specific calendars may also provide identifiers forpatterns and/or may comprise characteristics of another patternidentified with another category, such as being attributes of thatpattern. A pattern's origin may also form part of an identificationschema. For example, whether a pattern was determined by the systemsoperators, clients (for example as a report) or was the outcome of atleast one machine learning technique. Identification of patterns may beformed into ontologies, taxonomies and other organizationalarrangements.

As the systems employ a sets of ledgers, including for events, time andidentity, there becomes an immutable repository of such information heldby these ledgers. This may be supported by further repositories 106, ofFIG. 1 and 740, 750, and 752 of FIG. 7. In this example, historical datacan be held in a specific repository, e.g. learned/historical/profiledata repository 752 of FIG. 7, which in general can include those setsof historical information that may be reused through the systemoperations. For example, a historical set of patterns pertaining to aspecific time of year, large social event or other situation with alarge contextual footprint may be retained and used to evaluate othersimilar occurrences and/or form the basis for predictions for similarevents.

An event's time can be used to associate such events with events fromother sources and with contextual information, such as traffic, weather,local news. This association can be done using the timeslots, expressedin some embodiments as EC's, recorded on the reference time ledger,effectively making the time association immutable and verifiable.

Sequences of events from the same vehicle can be used to identify thestart and the end of a journey, for example from VHU provided (directlyor indirectly) vehicle operating parameters, such as engine start,engine stop, velocity and the like. This journey may involve one or moreexperience for the occupant involving one or more media sources that maybe determined to be experience sessions, such as listening to a radiostation or making a phone call.

For example a specific sequences of events may be used to determine thestart and end of a journey and may also include other engine ignition onand engine ignition off events, for example when filling a vehicle withfuel, stopping briefly at a specific destination (for example a school,shop and or the like), where these events may be integrated into ajourney based, in part on the location information sets provided by avehicle and/or other location tracking systems (such as a cellphonesGPS). In some embodiments each identified journey may be stored in arepository and aligned with one or more time reference ledgers and/oridentity ledgers and be configured so as to include at least oneexperience sessions.

Event locations, experience sessions, journeys and contextualinformation for one specific vehicle can be combined in order toidentify common activity patterns, such as common routes, commonlistening or other experience behaviors, preferred methods of userinteraction, etc., all under specific times and context combinationsaligned to one or more time reference and/or identity ledgers.

In one exemplary example, for a particular vehicle, common journeys,including their start and end locations at specific times during weekdays can be identified. Using location information, the most commonroutes taken by the vehicle during those common journeys can beobtained, as well as common experience sessions, during those commonjourneys. These repeated behaviors may form the basis for detecting anddetermining divergence from these patterns, which in some embodimentsmay be correlated to experience session to identify attribution, such asthat caused by single or multiple exposure to an advertisement or otherpromotion experienced during such a session within a journey. Such anapproach can support the capability to identify variations in thesecommon patterns and find one or more correlations between thesevariations and matching contextual information.

In some embodiments, data from the identified listening experiences frommultiple vehicles can be parsed and transformed into demographic andgeographic based metrics that are commonly utilized by the audioindustry into various forms of data products including data dashboardapplications 770, data access APIs 772, and report generation 774, asshown in at least FIG. 7. The data can be output as a report 774, asshown in which may include such metrics as, but not limited to: averagequarter hour (AQH), AQH Rating, unique number of listeners (CUME), CUMERating, and time spent listening (TSL). Metrics for each audio sourceare calculated by examining the listening experience time, duration inseconds, geography and the vehicle's unique identity.

In some embodiments, event information may be provided by anintermediate process, for example a set of VHU data may be aggregatedand/or processed. This processing may include anonymization of one ormore of the information parameters provided by the VHU, for example theidentity of the VHU, vehicle, location and the like. Further thisprocessing may include providing quantized stream of such information,where for example the information set is broken into discrete timesegments, for example 1 minute.

In some embodiments, calculated metrics that are the result ofprocessing matching and analytics and/or other evaluations,configurations, formatting or other processing, may be stored asaggregate information sets, using multiple aggregation criteria, such asmarket, demographic, media source, station, date, and the like in anappropriate repository 750. This aggregated information sets may then beused to produce reports 770, 772, 774, for data analysis andvisualization dashboards or for integration with decision making supportsystems within industry organizations (radio networks, media streamingcompanies, advertisers, etc.)

Individual vehicle and/or listener identity information, includingunique vehicle identification and/or driver or other occupantidentification, can be recorded in an identity distributed ledger, suchas a blockchain, along with various attributes and references toexternally stored information for the vehicle and or listener.Attributes of a vehicle and/or listener can be stored in the identityledger. Some of these attributes can be cryptographically signed by anattribute source, for example using a public-key cryptography. Thissupports third-party verification of attributes, by storing theattribute (claim) and the verification key (such a cryptographic publickey) of the attribute source (claim issuer). All attribute changes andany addition of new attributes can be done as new records in theidentity ledger, providing an immutable historical record of suchchanges.

Personal identifiable information (PII) of the vehicle and/or listenercan be securely kept in digitally encrypted storage apart from theidentity ledger, while the public keys are kept in the ledger. Thisenables a verifiable, trackable way of sharing PII with specificauthorized parties while keeping PII unavailable for unauthorized ones.An access key can be created with such mechanisms as to make themrevocable or valid only for a limited period of time. Revoking keys orremoving external the PII storage, partially or completely, aremechanisms that support implementation of requests to “forget” anindividual or his/her personal information within the system (possibleimplementations of “right to be forgotten” regulations in somecountries).

In one example, a distributed ledger, such as a blockchain, can beunderstood for the purpose of this invention as any system thatimplements a fully-automatized distributed digital ledger that is usedto record and synchronize transactions across computing devices (ornodes) connected through a computer network. Each node in thedistributed ledger can have a unique identity which controlsparticipation and access to the ledger. Agreement, based on awell-defined consensus mechanism, is required from all network membersto validate and approve new transactions, and only approved transactionsare recorded in the ledger. No one individual can delete or alter atransaction after they have been recorded. Such mechanism results in animmutable, distributed record of every transaction which can be used asa decentralized source of truth for the history recorded therein.

A distributed ledger can be used as a reference time ledger, asdiscussed above, storing timeslots along with references (uniqueidentifier, cryptographic hash, cryptographic keys and/or a combinationthereof) of events, event groups, event frameworks and/or eventcontainers. This configuration creates a mechanism to verify timereferences, time alignment and time corrections for events, eventgroups, contextual data, as well as aggregates and other data processingoutputs. For example, in FIG. 2 illustrates reference time ledgers usingdiffering time granularities 202, 203, 204, where the event containers210, 212, 214 match these granularities. This may be the case where aset of information has been received from a VHU proxy, such as thatprovided by a vehicle OEM or third party, where the timing granularityis in excess of that commonly used within the system, for example if theinformation sets has time granularity of a minute and the system usestiming of a second. The reference time source 201 may be used tosynchronize these timings, with any offsets, and a time reference ledgermay be instantiated with the granularity of the proxy information set,for example a minute, to immutably record these events. On subsequentevent and correlated timing information becoming available, for exampleform third party sources with higher granularity, these events may befurther related to other event containers and/or time reference ledgerswith higher timing granularity.

Another distributed ledger can be used as an identity ledger recordingidentity information, attributes, providing a way to keep track ofvehicles' and/or listeners' attributes. References and access keys toexternally stored to personal identifiable information (PII) can also bestored in the identity ledger. The PII can be stored separately usingaccess control mechanisms such as encryption and access controlledstorage subsystems. The identity ledger 205 can share a common trustedtime reference 201 with the reference time ledgers 202, 203, 204.

In some embodiments, each block chain block may have a time period thatis fixed or arbitrary in length, for example as shown in FIG. 13, a timereference ledger 1301 as illustrated by each block T1-Tn. Each of theseblocks T1-Tn, can include the timing information recorded from a trustedreference time source in a trusted, authenticated and authorized manner,can be bound to one or more event containers, which have timeinformation that includes the time information stored in the block. Thisbinding function, as shown as B1, B2, B3, B4, may be undertaken usingstandard cryptographic techniques, including hash functions, public key,symmetric key and/or other applicable techniques in any arrangementknown in the art. Once this binding B1-B4 has been undertaken each ofthe event containers bound to blocks for a certain period, asillustrated in FIG. 1320, 1322, 1324 can each have at least on eventframework bound to it, using similar cryptographic binding techniques.In this manner, the event information received from a VHU or proxy witha time signature can be aligned to an immutable ledger providing averifiable record of that event with the advantage of the informationset of that event being extensible as further event information becomesavailable.

The collection of event information sets from a single source, such as aVHU or proxy thereof can represent only a partial perspective on themedia experience for at least one occupant in a vehicle. FIG. 14illustrates the addition of contextual information 1401 to a set ofmedia events 1402 and vehicle events 1403, where this contextualinformation 1401 may be available in real time, for example a vehicleevent 1403 where windscreen washers are activated provides weatherinformation about rain, or after the event has been recorded, forexample when traffic information becomes available. In this example, themedia event 1402 can be extended by other media information that hasbeen correlated to that event, for example from a radio station programlog or similar media sources 1420. These event frameworks 1402, 1403 caninclude the contextual information are written and bound to an eventcontainer 1410 for an appropriate time period and this in turn can bebound to an immutable record, such as a reference time ledger 1430. Thisprovides the means for an event to bound to an immutable record in amanner that is extensible and verifiable.

In some embodiments the metrics and data aggregations that result fromthe event data processing, matching and analytics pipeline 720, as shownin FIG. 7, may be stored in a data repository for an aggregate andintegration module 750. The data then can be used to produce reports770, 772, 774 for customers, which include summaries of metricsbehaviors filtered, aggregated and organized according to parameterssuch as markets, media sources, demographics, time periods and time ofday, which are of particular interest to specific customers and markets.

The resulting data may also be presented to customers and end usersthrough data visualization dashboard software application on a desktopcomputer or mobile computing device. Such data visualization dashboardmay include multiple options for slicing and dicing the data resultsbased on markets, media sources, demographics, time periods and time ofday and so on. There may be different dashboards, with focus onparticular subsets of resulting data, for different customer markets.For example, there may be vehicle OEM-focused dashboards, radio industryfocused dashboards, advertising industry dashboards, music industrydashboards, etc.

Additionally, the resulting data in the aggregate and integration module750 may be combined with reference data, such as markets, sources,population and demographics, and organized in multi-dimensionalstructures such as a data cube, where the data may be structured intomultiple dimensions, which may include time, markets, demographics,vehicle brands and model, media content source, content format, and thelike, using approaches such as star schema. The resulting data organizedin this matter can then be made available to customers, for example viaapplication programming interfaces (APIs) 772, so customers canintegrate it into their own analytics systems and processes for theirown data research and analytics, using approaches such as OLAP, datamarts, predictive analytics or machine learning. Access to the resultingdata can include access control, in order to, for example, provideaccess only to markets licensed by customers and to provide access toPII only to parties approved by end users or government.

APIs may also be created to provide customers with controlled access tothe time reference ledger and to the Identity ledger, so the resultingdata and its attributes, such as its sources, time accuracy, appliedmethods and the like can be verified.

It should be noted that the present invention is described and shown inconnection with the monitoring and analysis of cross channel consumptionin a vehicle but it should be understood the present invention hasapplicability in any environment or location.

It would be appreciated by those skilled in the art that various changesand modifications can be made to the illustrated embodiments withoutdeparting from the spirit of the present invention. All suchmodifications and changes are intended to be covered by the appendedclaims.

What is claimed is:
 1. A method for measuring and analyzing in vehiclemedia consumption and user interaction with a vehicle through an invehicle entertainment system located in the vehicle, the methodcomprising steps of: receiving media content through the in vehicleentertainment system, the in vehicle entertainment system is configuredas a head unit located in the vehicle, wherein the head unit furtherincludes a computing device in the head unit with storage, a display,operation controls, a microprocessor, a memory component, I/O inputs andoutputs and an operating system installed and running thereon;monitoring both the media content and the user interaction with thevehicle in response to the media content, with content and interactionmeasurement software stored in the storage of the head unit of thevehicle as a module on the in vehicle entertainment system; the contentand interaction measurement software directly recording data relative tothe media content being played and the user interaction, in real time,as a data set; and transmitting the data set relative to the mediacontent being played and user interaction to at least one immutabledistributed ledger; wherein the data set includes at least a local timeof a start of the media content or the user interaction; and wherein thedata set is hashed and bound to the at least one immutable distributedledger.
 2. The method of claim 1, further comprising generating areport, based on the data set, relating to the user interaction with themedia content and the vehicle; and determining a set of consumption anduse habits of at least one user from the report.
 3. The method of claim1, wherein the data set comprises at least one of: vehicleidentification parameters, vehicle position parameters, vehicleoperation parameters, media source identification parameters,consumption parameters, connected consumer electronic device parameters,smartphone integration parameters, unique identification parameters, invehicle entertainment system parameters, vehicle system parameters,content parameters, contextual data parameters, and advertisingparameters.
 4. The method of claim 1, further comprising processing thedata by analyzing a set of consumption and use habits corresponding toat least one user and analyzing a set of situational consumption and usehabits corresponding to the at least one user corresponding to a set ofsituations.
 5. The method of claim 4, wherein the data may be obfuscatedfor a purpose of privacy.
 6. The method of claim 4, wherein the set ofconsumption and use habits may be determined through repeated patternsin event set data stored in the at least one immutable distributedledger.
 7. The method of claim 1, further comprises steps of: storingthe data on the memory component of the in vehicle entertainment system;and periodically transmitting the data to one of the at least oneimmutable distributed ledger and another repositories connected to theat least one immutable distributed ledger.
 8. The method of claim 7,wherein time and frequency of the step of periodically transmitting thedata is configurable.
 9. The method of claim 1, wherein the data setcomprises an information set which is bound to a reference time and areference location.
 10. The method of claim 9, further comprisingnormalizing the reference time to conform to a reference time ledger toimmutably bind the reference time.
 11. The method of claim 9, furthercomprising normalizing the reference location to conform to a referencelocation ledger to immutably bind the reference location.
 12. The methodof claim 9, wherein the data set further includes at least one uniqueidentifier.
 13. A media consumption and user interaction measurementsystem for measuring and analyzing in vehicle media consumption and userinteraction, the system comprising: a vehicle; an in vehicleentertainment system configured as a head unit in the vehicle, the headunit including a computing device with storage, a display, operationcontrols, a microprocessor, a memory component, I/O inputs and anoperating system, the in vehicle entertainment system being operable toselectively receive media content; content and interaction measurementsoftware stored on the memory component of the in vehicle entertainmentsystem as a module, the content and interaction measurement softwarebeing operable by the processor and being configured to directly recorddata relative to the media consumption and the user interaction, in realtime, as a data set; the module being configured and arranged formonitoring the media consumption and the user interaction with thevehicle in response to the media content, with the computing device; andan immutable distributed ledger configured and arranged to receive andstore a data set from the vehicle head unit, wherein the data setincludes at least a local time of a start of the media content or theuser interaction, and wherein the data set is hashed and bound to theimmutable distributed ledger.
 14. The measurement system of claim 13,wherein the data set comprises at least one of: vehicle identificationparameters, vehicle position parameters, vehicle operation parameters,media source identification parameters, consumption parameters,connected consumer electronic device parameters, smartphone integrationparameters, unique identification parameters, in vehicle entertainmentsystem parameters, vehicle system parameters, content parameters,contextual data parameters, and advertising parameters.
 15. Themeasurement system of claim 13, wherein the system is configured toprocess the data set received by at least one repository connected tothe immutable distributed ledger and is configured to generate reportsbased on the data processed.
 16. The measurement system of claim 13,wherein the data set comprises an information set which is bound to areference time and a reference location.
 17. The measurement system ofclaim 16, wherein the reference time is normalized to conform to areference time ledger to immutably bind the reference time.
 18. Themeasurement system of claim 16, wherein the reference location isnormalized to conform to a reference location ledger to immutably bindthe reference location.
 19. The measurement system of claim 16, whereinthe information set further includes at least one unique identifier. 20.The measurement system of claim 13, wherein the data set iscryptographically bound to the immutable distributed ledger.
 21. Themeasurement system of claim 13, wherein the data set is stored in arepository which is cryptographically bound to the immutable distributedledger.
 22. The measurement system of claim 13, wherein the data set isstored in a repository which is cryptographically bound to the immutabledistributed ledger, and wherein the data set is encrypted.
 23. Themeasurement system of claim 13, wherein the data set is stored in arepository which is cryptographically bound to the immutable distributedledger, wherein the data set is encrypted, and wherein at least oneencryption key is stored in the immutable distributed ledger or a hashof the at least one encryption key is stored in the immutabledistributed ledger.
 24. The measurement system of claim 13, wherein userinteraction includes interaction of a plurality of users with thevehicle.
 25. The measurement system of claim 13, wherein the system isconfigured and arranged to establish typical patterns of behavior basedon the data, and wherein the system is configured and arranged tomeasure an impact of an in vehicle experience on the user by comparingthe typical pattern of behavior and a reaction of the user to theexperience.
 26. A method for measuring and analyzing in vehicle mediaconsumption and user interaction with a vehicle through an in vehicleentertainment system located in the vehicle, the method comprising stepsof: receiving media content through the in vehicle entertainment system,the in vehicle entertainment system is configured as a head unit locatedin the vehicle, wherein the head unit further includes a computingdevice in the head unit with storage, a display, operation controls, amicroprocessor, a memory component, I/O inputs and outputs and anoperating system installed and running thereon; monitoring both themedia content and the user interaction with the vehicle in response tothe media content, with content and interaction measurement softwarestored in the storage of the head unit of the vehicle as a module on thein vehicle entertainment system; the content and interaction measurementsoftware directly recording data relative to the media content beingplayed and the user interaction, in real time, as an event set; andtransmitting the event set relative to the media content being playedand the user interaction to at least one immutable distributed ledger;wherein the event set includes at least a local time of a start of theevent set; wherein the event set is hashed and bound to the at least oneimmutable distributed ledger; and wherein the hash is identified as aroot of trust for the event set.
 27. The method of claim 26, wherein theevent set is stored in the at least one immutable distributed ledger anda hashed representation of the event set configured as a verification ofevents occurring over a specific time period.
 28. The method of claim26, wherein the event set is stored in the at least one immutabledistributed ledger and a hashed representation of the event set isconfigured as verification of events occurring at a set of locations.29. The method of claim 26, wherein the event set is stored in the atleast one immutable distributed ledger and hashed representation of theevent set is configured to support normalization of further data setscorrelated to the event set.